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Master of Science - Thesis

The Master of Science - Thesis program is intended for students interested in careers in industry, the public sector, and academia. It provides a short-term research experience leading to the preparation and defense of a research-based thesis.

In order to enroll in the MS Thesis program, you must first secure a thesis advisor. You should consult with your thesis and graduate advisors for course selection recommendations. As a student in the MS Thesis program, you can enroll in any combination of Robotics graduate courses.

MS Thesis students must complete the following requirements to obtain the MS degree:

MS Professional (Non-Thesis) Degree Requirements. Students must fulfill all of the non-thesis MS Degree requirements.
MS Thesis. 6 credits of MS thesis hours (ROBO 6xxx) must be completed, typically in the final two semesters of the program. Students are not able to register for MS thesis credits on their own and should submit a request for thesis hours through the Thesis/Dissertation Hours Enrollment Request Form.
Thesis Advisor Selection. Upon finding a research advisor, you must complete the MS Thesis Research Expectations Form. This form should be completed no later than the end of the first semester of enrollment in the MS Thesis program.
The oral thesis defense must be passed about 2/3 into the last semester.
The written thesis, electronically; and
The physical signature page, signed by all committee members, to the Graduate School.
Submit both of these items on the Thesis Approval Form website.
Thesis Defense. You must pass a thesis defense, which is a final examination on the thesis and related topics. In the defense, you are expected to explain your research clearly and concisely, and to discuss how it relates to other research in the field. This is an opportunity for recognition of completed MS Thesis research. It is also an opportunity for discussion and formal evaluation of the thesis.

Defending Your Thesis

The thesis defense may occur before or after the final electronic submission of the written thesis to the Graduate School, but must take place prior to the end of your final semester. Failure to defend prior to the end of the proposed final semester may result in the need to register for additional course credits during another semester. All required forms should be submitted on time according to the following deadlines:

To the Department: The Master’s Examination Report should be submitted to your graduate advisor at least 3 weeks prior to the defense.
To the Committee: The written thesis should be sent as a single pdf file by email to all members of the defense committee, as well as to the graduate advisors at [email protected] , at least one week before the defense. This deadline is intended to allow the defense committee sufficient time to review the thesis and to formulate questions and feedback. Prior to the defense, you should contact all members of the committee to assess their areas of interest and concerns. This will help you anticipate any questions that will be asked.
You must be registered as full time, regular degree-seeking student during the semester in which you pass the examination. The examination is conducted by a committee appointed by the thesis advisor and approved by the Dean of the Graduate School, and consists of at least three people, two of which must be ROBO faculty.
The chair of the committee must have a regular or tenured Graduate Faculty appointment. The other committee members must have either regular or special Graduate Faculty appointments. Please contact the graduate advisors at [email protected] as soon as you form your committee, and no later than 6 weeks prior to your examination, to verify that the necessary appointments are in place. It takes 2-4 weeks to process a faculty appointment. You should submit a recent CV for any committee member who does not have a faculty appointment to the graduate advisors as soon as possible.
You should coordinate scheduling the examination with the committee, and should schedule the examination for one hour and a half. The examination is wholly oral and open to the public for the first portion of the examination. You must prepare a professional oral presentation that covers what was written in the thesis. This presentation should be 45 minutes in length. This presentation shall be delivered at the final examination to the examination committee. The oral presentation portion of the examination is open to all students and faculty. Questions are entertained at the end of the presentation. The final part of the examination is closed to only the student and the examination committee. During this portion, questions are entertained that cover the field of concentration and related fields. More than one dissenting vote among the committee constitutes an unsatisfactory exam. A student who fails the exam may attempt it once more after a period of time determined by the committee.

Transfer Credit

You may be eligible to transfer up to 9 hours of coursework to meet Master’s degree course requirements. More information is available on the Request for Transfer of Credit Form from the CU Graduate School. To transfer credits, you must fill out and submit this form to the Graduate School via DocuSign.

Please note that requests for transfer credit can only be made after completing 6 credits of graduate level coursework at CU Boulder. These requests should be submitted as soon after completion of this 6 credit requirement as possible. Typically, this means that transfer of credit requests are processed during the second semester of study at CU Boulder.

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MSE in Robotics

Robotics Program

The University of Pennsylvania’s School of Engineering and Applied Science offers a unique master’s degree in Robotics (ROBO). This multi-disciplinary program is jointly sponsored by the Departments of Computer and Information Science, Electrical and Systems Engineering, and Mechanical Engineering and Applied Mechanics.

Housed in and administered by the GRASP Lab , one of the top robotics research centers in the world, Penn Engineering’s ROBO master’s program educates students in the science and technology of robotics, vision, perception, control, automation, and machine learning. Our students hail from a variety of engineering, scientific, and mathematical backgrounds, united by a passion for robots and a desire to advance robotic technologies to benefit humanity. Our program provides an ideal foundation for jobs in a variety of industries including robotics, aerospace, automotive, industrial automation and defense; it also provides a solid basis for further graduate studies.

The modern expert in robotics and intelligent systems must be proficient in artificial intelligence, machine learning, computer vision, control systems, kinematics, and dynamics, as well as the design, programming, and prototyping of robotic systems. Our flexible program offers the breadth and depth needed for this type of interdisciplinary training. Features of the program include:

Course instruction, advising, and master’s thesis supervision by world-renowned robotics faculty.
A total of ten courses in the areas of artificial intelligence, robot design and analysis, controls, and perception, with advanced electives and an optional master’s thesis project.
Access to state-of-the-art experimental facilities in the GRASP Lab and opportunities for research projects in robotics and related fields.
Participation in weekly GRASP seminars and lunches highlighting cutting-edge advances in robotics research and education.
Professional networking with peers in industry and academia, an ideal preparation for doctoral studies in robotics and related fields.

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Doctoral Program
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MSE in Computer Graphics and Game Technology
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Your CIS Contacts:

Redian Furxhiu Graduate Coordinator for on-campus MCIT, CIS/MSE and CGGT programs Office: 308 Levine Phone: 215-898-1668 Email: [email protected]

Staci Kaplan Program Manager for DATS (Data Science MSE) Office: 308 Levine Phone: 215-573-2431 Email: [email protected]

Britton Carnevali Doctoral Program Manager Office: 310 Levine Phone: 215-898-5515 Email: [email protected]

Mariel Celentano Graduate Coordinator for ROBO Office: 459 Levine Phone: 215-573-4907 Email: [email protected]

Liz Wai-Ping Ng Associate Director for Embedded Systems MSE program Office: 313 Levine Phone: 215-898-8543 Email: [email protected]

Julia Esposito PICS Program Coordinator, SCMP Academic Coordinator Office: 3401 Walnut, 5th Fl. Phone: 215-573-6037 Email: [email protected]

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Currently Available Theses Topics

We offer these current topics directly for Bachelor and Master students at TU Darmstadt who can feel free to DIRECTLY contact the thesis advisor if you are interested in one of these topics. Excellent external students from another university may be accepted but are required to first email Jan Peters before contacting any other lab member for a thesis topic. Note that we cannot provide funding for any of these theses projects.

We highly recommend that you do either our robotics and machine learning lectures ( Robot Learning , Statistical Machine Learning ) or our colleagues ( Grundlagen der Robotik , Probabilistic Graphical Models and/or Deep Learning). Even more important to us is that you take both Robot Learning: Integrated Project, Part 1 (Literature Review and Simulation Studies) and Part 2 (Evaluation and Submission to a Conference) before doing a thesis with us.

In addition, we are usually happy to devise new topics on request to suit the abilities of excellent students. Please DIRECTLY contact the thesis advisor if you are interested in one of these topics. When you contact the advisor, it would be nice if you could mention (1) WHY you are interested in the topic (dreams, parts of the problem, etc), and (2) WHAT makes you special for the projects (e.g., class work, project experience, special programming or math skills, prior work, etc.). Supplementary materials (CV, grades, etc) are highly appreciated. Of course, such materials are not mandatory but they help the advisor to see whether the topic is too easy, just about right or too hard for you.

Only contact *ONE* potential advisor at the same time! If you contact a second one without first concluding discussions with the first advisor (i.e., decide for or against the thesis with her or him), we may not consider you at all. Only if you are super excited for at most two topics send an email to both supervisors, so that the supervisors are aware of the additional interest.

FOR FB16+FB18 STUDENTS: Students from other depts at TU Darmstadt (e.g., ME, EE, IST), you need an additional formal supervisor who officially issues the topic. Please do not try to arrange your home dept advisor by yourself but let the supervising IAS member get in touch with that person instead. Multiple professors from other depts have complained that they were asked to co-supervise before getting contacted by our advising lab member.

NEW THESES START HERE

Blending Deep Generative Models using Stochastic Optimization

Topic: This Master Thesis aims to explore the blending of deep generative models using stochastic optimization techniques [1], focusing on reactive motion generation for robotics. The research will encompass the training of deep generative models, such as Score-based [2], or Flow-based models, specifically utilizing the JAX framework for efficient computation. A significant part of the thesis will involve deriving a mixture of experts algorithm, which leverages these trained generative models in combination with other manually specified objectives to enhance the performance of the motion generator. This integration aims to create more adaptive and responsive robotic behaviors in dynamic environments, offering a substantial advancement over existing methods.

Requirements

Strong Python programming skills
Knowledge in Machine Learning
Experience with deep learning libraries and JAX is a plus

Interested students can apply by sending an e-mail to [email protected] and attaching the documents mentioned below:

Curriculum Vitae
Motivation letter explaining why you would like to work on this topic and why you are the perfect candidate

References [1] Hansel, K.; Urain, J.; Peters, J.; Chalvatzaki, G. (2023). Hierarchical Policy Blending as Inference for Reactive Robot Control, 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE. [2] Urain, J.; Funk, N.; Peters, J.; Chalvatzaki G (2023). SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion, International Conference on Robotics and Automation (ICRA).

Self-supervised learning of a visual object-centric representation for robotic manipulation

Scope: External Master Thesis 🇫🇷 This master thesis will be conducted with our French partners at Ecole Centrale de Lyon . Possibility of ERASMUS scholarship. Advisor: Alexandre Chapin , Liming Chen , Emmanuel Dellandrea Added: 2024-07-15 Start: ASAP Topic:

Vision-based learning for robotic manipulation often relies on holistic visual scene representations, where the environment is depicted as a single vector. This method is suboptimal for handling diverse scenes and objects in unconstrained environments. Better representations can improve generalization and data efficiency in robotic learning [1]. Inspired by human perception, object-centric representation has been developed to represent environments with multiple vectors, each corresponding to an object's properties [2]. However, these methods mainly use synthetic datasets [3, 4, 5] and struggle with real-world scenarios [6]. With advances in self-supervised learning for vision models [7, 8], which show promise for object discovery, we propose pre-training an object-centric representation using self-supervised methods to scale to real-world scenarios. This thesis will focus on: Developing and training an object-centric self-supervised model on a real-world dataset. Pre-training the model on a real-world robotic dataset. Applying the pre-trained model to visual-based robotic manipulation tasks.

Interested students can apply by sending the required documents to [email protected] and attaching the required documents mentioned below.

Experience with the Pytorch library

Preferred Qualifications

Prior experience in Computer Vision and/or Robotics is preferred
Use of distributed environment for learning of models (SLURM)
Knowledge on recent self-supervised learning methods for vision [7, 8]

Required Documents

References [1] O. Kroemer et al. “A review of robot learning for manipulation: Challenges, representations, and algorithms” (2019) [2] F. Locatello et al. “Object-centric learning with Slot Attention” (2020) [3] G. Singh et al. “Illiterate DALL-E learns to compose” (2021) [4] T. Kipf et al. “Conditional object-centric learning from video” (2022) [5] G. Singh et al. “Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos” (2022) [6] Z. Wu et al. “SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models” (2023) [7] M. Caron et al. “Emerging Properties in Self-Supervised Vision Transformers” (2021) [8] O. J. Hénaff et al. “Object discovery and representation networks” (2022)

Data-Driven Bimanual Robotic Grasping

Scope: Bachelor/Master thesis Advisor: Vignesh Prasad and Alap Kshirsagar Added: 2024-04-25 Start: ASAP Topic:

Grasping is one of the most fundamental and challenging tasks in the robotic manipulation of objects. Most of the prior work on robotic grasping has focused on grasping with a single gripper and several large-scale datasets have been developed in recent years to tackle the problem of single-arm grasping in 3D by utilizing deep-learning techniques [1,2]. But many tasks in industrial and domestic environments require bimanual grasps. Bimanual grasps are required for manipulation of large, deformable or fragile objects. This project seeks to develop a data-driven technique for bimanual robotic grasp generation from visual input. We will utilize a large-scale dataset of simulated bimanual grasps [3] to train a bimanual grasp pose generation model. The method will be evaluated in simulation as well as on a real robot.

Knowledge in Machine Learning / Supervised Learning
Experience with deep learning libraries is a plus

Interested students can apply by sending an e-mail to [email protected] and attaching the documents mentioned below:

References [1] C. Eppner, A. Mousavian, and D. Fox, “ACRONYM: A Large-Scale Grasp Dataset Based on Simulation,” in Proceedings - IEEE International Conference on Robotics and Automation, 2021, vol. 2021-May, pp. 6222–6227, doi: 10.1109/ICRA48506.2021.9560844. [2] A. Mousavian, C. Eppner, and Di. Fox, “6-DOF GraspNet: Variational grasp generation for object manipulation,” in Proceedings of the IEEE International Conference on Computer Vision, 2019, vol. 2019-Octob, pp. 2901–2910, doi: 10.1109/ICCV.2019.00299. [3] G. Zhai et al., “{DA2} Dataset: Toward Dexterity-Aware Dual-Arm Grasping,” IEEE Robot. Autom. Lett., vol. 7, no. 4, pp. 8941–8948, 2022.

Imitation Learning for High-Speed Robot Air Hockey

Scope: Master thesis Advisor: Puze Liu and Julen Urain De Jesus Start: ASAP Topic:

High-speed reactive motion is one of the fundamental capabilities of robots to achieve human-level behavior. Optimization-based methods suffer from real-time requirement when the problem is non-convex and contains constraints. Reinforcement learning requires extensive reward engineering to achieve the desired performance. Imitation learning, on the other hand, gathers human knowledge directly from data collection and enables robots to learn natural movements efficiently. In this paper, we explore how imitation learning can be performed in a complex robot Air Hockey Task. The robot needs to learn not only low-level skills, but also high-level tactics from human demonstrations.

Good Knowledge in Robotics

References * Chi, Cheng, et al. "Diffusion policy: Visuomotor policy learning via action diffusion." arXiv preprint arXiv:2303.04137 (2023). * Liu, Puze, et al. "Robot reinforcement learning on the constraint manifold." Conference on Robot Learning. PMLR (2022). * Pan, Yunpeng, et al. "Imitation learning for agile autonomous driving." The International Journal of Robotics Research 39.2-3 (2020). Interested students can apply by sending an e-mail to [email protected] and attaching the required documents mentioned above.

Walk your network: investigating neural network’s location in Q-learning methods.

Scope: Master thesis Advisor: Theo Vincent Start: Flexible Topic:

Q-learning methods are at the heart of Reinforcement Learning. They have been shown to outperform humans on some complex tasks such as playing video games [1]. In robotics, where the action space is in most cases continuous, actor-critic methods are relying on Q-learning methods to learn the critic [2]. Although Q-learning methods have been extensively studied in the past, little focus has been placed on the way the online neural network is exploring the space of Q functions. Most approaches focus on crafting a loss that would make the agent learn better policies [3]. Here, we offer a thesis that focuses on the position of the online Q neural network in the space of Q functions. The student will first investigate this idea on simple problems before comparing the performance to strong baselines such as DQN or REM [1, 4] on Atari games. Depending on the result, the student might as well get into MuJoCo and compare the results with SAC [2]. The student will be welcome to propose some ideas as well.

Highly motivated students can apply by sending an email to [email protected] . Please attach your CV, a grade sheet and clearly state why you are interested in this topic. Students who have followed the Reinforcement Learning or Robot Learning course will be prioritized.

Knowledge in Reinforcement Learning

References [1] Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." nature 518.7540 (2015): 529-533. [2] Haarnoja, Tuomas, et al. "Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor." International conference on machine learning. PMLR, 2018. [3] Hessel, Matteo, et al. "Rainbow: Combining improvements in deep reinforcement learning." Proceedings of the AAAI conference on artificial intelligence. Vol. 32. No. 1. 2018. [4] Agarwal, R., Schuurmans, D. & Norouzi, M.. (2020). An Optimistic Perspective on Offline Reinforcement Learning International Conference on Machine Learning (ICML).

Co-optimizing Hand and Action for Robotic Grasping of Deformable objects

This project aims to advance deformable object manipulation by co-optimizing robot gripper morphology and control policies. The project will involve utilizing existing simulation environments for deformable object manipulation [2] and implementing a method to jointly optimize gripper morphology and grasp policies within the simulation.

Required Qualification:

Familiarity with deep learning libraries such as PyTorch or Tensorflow

Preferred Qualification:

Attendance of the lectures "Statistical Machine Learning", "Computational Engineering and Robotics" and "Robot Learning"

Application Requirements:

Interested students can apply by sending an e-mail to [email protected] and attaching the required documents mentioned above.

References: [1] Xu, Jie, et al. "An End-to-End Differentiable Framework for Contact-Aware Robot Design." Robotics: Science & Systems. 2021. [2] Huang, Isabella, et al. "DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets." arXiv preprint arXiv:2303.16138 (2023).

Geometry-Aware Diffusion Models for Robotics

In this thesis, you will work on developing an imitation learning algorithm using diffusion models for robotic manipulation tasks, such as the ones in [2, 3, 4], but taking into account the geometry of the task space.

If this sounds interesting, please send an email to [email protected] and [email protected] , and possibly attach your CV, highlighting the relevant courses you took in robotics and machine learning.

What's in it for you:

You get to work on an exciting topic at the intersection of deep-learning and robotics
We will supervise you closely throughout your thesis
Depending on the results, we will aim for an international conference publication

Requirements:

Be motivated -- we will support you a lot, but we expect you to contribute a lot too
Robotics knowledge
Experience setting up deep learning pipelines -- from data collection, architecture design, training, and evaluation
PyTorch -- especially experience writing good parallelizable code (i.e., runs fast in the GPU)

References: [1] https://arxiv.org/abs/2112.10752 [2] https://arxiv.org/abs/2308.01557 [3] https://arxiv.org/abs/2209.03855 [4] https://arxiv.org/abs/2303.04137 [5] https://arxiv.org/abs/2205.09991

Learning Latent Representations for Embodied Agents

Interested students can apply by sending an E-Mail to [email protected] and attaching the required documents mentioned below.

Experience with TensorFlow/PyTorch
Familiarity with core Machine Learning topics
Experience programming/controlling robots (either simulated or real world)
Knowledgeable about different robot platforms (quadrupeds and bipedal robots)
Resume / CV
Cover letter explaining why this topic fits you well and why you are an ideal candidate

References: [1] Ho and Ermon. "Generative adversarial imitation learning" [2] Arenz, et al. "Efficient Gradient-Free Variational Inference using Policy Search"

Characterizing Fear-induced Adaptation of Balance by Inverse Reinforcement Learning

Interested students can apply by sending an E-Mail to [email protected] and attaching the required documents mentioned below.

Basic knowledge of reinforcement learning
Hand-on experience with reinforcement learning or inverse reinforcement learning
Cognitive science background

References: [1] Maki, et al. "Fear of Falling and Postural Performance in the Elderly" [2] Davis et al. "The relationship between fear of falling and human postural control" [3] Ho and Ermon. "Generative adversarial imitation learning"

Timing is Key: CPGs for regularizing Quadruped Gaits learned with DRL

To tackle this problem we want to utilize Central Pattern Generators (CPGs), which can generate timings for ground contacts for the four feet. The policy gets rewarded for complying with the contact patterns of the CPGs. This leads to a straightforward way of regularizing and steering the policy to a natural gait without posing too strong restrictions on it. We first want to manually find fitting CPG parameters for different gait velocities and later move to learning those parameters in an end-to-end fashion.

Highly motivated students can apply by sending an E-Mail to [email protected] and attaching the required documents mentioned below.

Minimum Qualification:

Good Python programming skills
Basic knowledge of the PyTorch library
Basic knowledge of Reinforcement Learning
Good knowledge of the PyTorch library
Basic knowledge of the MuJoCo simulator

References: [1] Cheng, Xuxin, et al. "Extreme Parkour with Legged Robots."

Damage-aware Reinforcement Learning for Deformable and Fragile Objects

Goal of this thesis will be the development and application of a model-based reinforcement learning method on real robots. Your tasks will include: 1. Setting up a simulation environment for deformable object manipulation 2. Utilizing existing models for stress and deformability prediction[1] 3. Implementing a reinforcement learning method to work in simulation and, if possible, on the real robot methods.

If you are interested in this thesis topic and believe you possess the necessary skills and qualifications, please submit your application, including a resume and a brief motivation letter explaining your interest and relevant experience. Please send your application to [email protected].

Required Qualification :

Enthusiasm for and experience in robotics, machine learning, and simulation
Strong programming skills in Python

Desired Qualification :

Attendance of the lectures "Statistical Machine Learning", "Computational Engineering and Robotics" and (optionally) "Robot Learning"

References: [1] Huang, I., Narang, Y., Bajcsy, R., Ramos, F., Hermans, T., & Fox, D. (2023). DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets. arXiv preprint arXiv:2303.16138.

Imitation Learning meets Diffusion Models for Robotics

The objective of this thesis is to build upon prior research [2, 3] to establish a connection between Diffusion Models and Imitation Learning. We aim to explore how to exploit Diffusion Models and improve the performance of Imitation learning algorithms that interact with the world.

We welcome highly motivated students to apply for this opportunity by sending an email expressing their interest to Firas Al-Hafez ( [email protected] ) Julen Urain ( [email protected] ). Please attach your letter of motivation and CV, and clearly state why you are interested in this topic and why you are the ideal candidate for this position.

Required Qualification : 1. Strong Python programming skills 2. Basic Knowledge in Imitation Learning 3. Interest in Diffusion models, Reinforcement Learning

Desired Qualification : 1. Attendance of the lectures "Statistical Machine Learning", "Computational Engineering and Robotics" and/or "Reinforcement Learning: From Fundamentals to the Deep Approaches"

References: [1] Song, Yang, and Stefano Ermon. "Generative modeling by estimating gradients of the data distribution." Advances in neural information processing systems 32 (2019). [2] Ho, Jonathan, and Stefano Ermon. "Generative adversarial imitation learning." Advances in neural information processing systems 29 (2016). [3] Garg, D., Chakraborty, S., Cundy, C., Song, J., & Ermon, S. (2021). Iq-learn: Inverse soft-q learning for imitation. Advances in Neural Information Processing Systems, 34, 4028-4039. [4] Chen, R. T., & Lipman, Y. (2023). Riemannian flow matching on general geometries. arXiv preprint arXiv:2302.03660.

Be extremely motivated -- we will support you a lot, but we expect you to contribute a lot too

Scaling Behavior Cloning to Humanoid Locomotion

Scope: Bachelor / Master thesis Advisor: Joe Watson Added: 2023-10-07 Start: ASAP Topic: In a previous project [1], I found that behavior cloning (BC) was a surprisingly poor baseline for imitating humanoid locomotion. I suspect the issue may lie in the challenges of regularizing high-dimensional regression.

The goal of this project is to investigate BC for humanoid imitation, understand the scaling issues present, and evaluate possible solutions, e.g. regularization strategies from the regression literature.

The project will be building off Google Deepmind's Acme library [2], which has BC algorithms and humanoid demonstration datasets [3] already implemented, and will serve as the foundation of the project.

To apply, email [email protected] , ideally with a CV and transcript so I can assess your suitability.

Experience, interest and enthusiasm for the intersection of robot learning and machine learning
Experience with Acme and JAX would be a benefit, but not necessary

References: [1] https://arxiv.org/abs/2305.16498 [2] https://github.com/google-deepmind/acme [3] https://arxiv.org/abs/2106.00672

Robot Gaze for Communicating Collision Avoidance Intent in Shared Workspaces

Scope: Bachelor/Master thesis Advisor: Alap Kshirsagar , Dorothea Koert Added: 2023-09-27 Start: ASAP

Topic: In order to operate close to non-experts, future robots require both an intuitive form of instruction accessible to lay users and the ability to react appropriately to a human co-worker. Instruction by imitation learning with probabilistic movement primitives (ProMPs) [1] allows capturing tasks by learning robot trajectories from demonstrations including the motion variability. However, appropriate responses to human co-workers during the execution of the learned movements are crucial for fluent task execution, perceived safety, and subjective comfort. To facilitate such appropriate responsive behaviors in human-robot interaction, the robot needs to be able to react to its human workspace co-inhabitant online during the execution. Also, the robot needs to communicate its motion intent to the human through non-verbal gestures such as eye and head gazes [2][3]. In particular for humanoid robots, combining motions of arms with expressive head and gaze directions is a promising approach that has not yet been extensively studied in related work.

Goals of the thesis:

Develop a method to combine robot head/gaze motion with ProMPs for online collision avoidance
Implement the method on a Franka-Emika Panda Robot
Evaluate and compare the implemented behaviors in a study with human participants

Highly motivated students can apply by sending an email to [email protected]. Please attach your CV and transcript, and clearly state your prior experiences and why you are interested in this topic.

Strong Programming Skills in python
Prior experience with Robot Operating System (ROS) and user studies would be beneficial
Strong motivation for human-centered robotics including design and implementation of a user study

References : [1] Koert, Dorothea, et al. "Learning intention aware online adaptation of movement primitives." IEEE Robotics and Automation Letters 4.4 (2019): 3719-3726. [2] Admoni, Henny, and Brian Scassellati. "Social eye gaze in human-robot interaction: a review." Journal of Human-Robot Interaction 6.1 (2017): 25-63. [3] Lemasurier, Gregory, et al. "Methods for expressing robot intent for human–robot collaboration in shared workspaces." ACM Transactions on Human-Robot Interaction (THRI) 10.4 (2021): 1-27.

Tactile Sensing for the Real World

Topic: Tactile sensing is a crucial sensing modality that allows humans to perform dexterous manipulation[1]. In recent years, the development of artificial tactile sensors has made substantial progress, with current models relying on cameras inside the fingertips to extract information about the points of contact [2]. However, robotic tactile sensing is still a largely unsolved topic despite these developments. A central challenge of tactile sensing is the extraction of usable representations of sensor readings, especially since these generally contain an incomplete view of the environment.

Recent model-based reinforcement learning methods like Dreamer [3] leverage latent state-space models to reason about the environment from partial and noisy observations. However, more work has yet to be done to apply such methods to real-world manipulation tasks. Hence, this thesis will explore whether Dreamer can solve challenging real-world manipulation tasks by leveraging tactile information. Initial results suggest that tasks like peg-in-a-hole can indeed be solved with Dreamer in simulation (see figure above), but the applicability of this method in the real world has yet to be shown.

In this work, you will work with state-of-the-art hardware and compute resources on a hot research topic with the option of publishing your work at a scientific conference.

Highly motivated students can apply by sending an email to [email protected]. Please attach a transcript of records and clearly state your prior experiences and why you are interested in this topic.

Ideally experience with deep learning libraries like JAX or PyTorch
Experience with reinforcement learning is a plus
Experience with Linux

References [1] 2S Match Anest2, Roland Johansson Lab (2005), https://www.youtube.com/watch?v=HH6QD0MgqDQ [2] Gelsight Inc., Gelsight Mini, https://www.gelsight.com/gelsightmini/ [3] Hafner, D., Lillicrap, T., Ba, J., & Norouzi, M. (2019). Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603.

Large Vision-Language Neural Networks for Open-Vocabulary Robotic Manipulation

Robots are expected to soon leave their factory/laboratory enclosures and operate autonomously in everyday unstructured environments such as households. Semantic information is especially important when considering real-world robotic applications where the robot needs to re-arrange objects as per a set of language instructions or human inputs (as shown in the figure). Many sophisticated semantic segmentation networks exist [1]. However, a challenge when using such methods in the real world is that the semantic classes rarely align perfectly with the language input received by the robot. For instance, a human language instruction might request a ‘glass’ or ‘water’, but the semantic classes detected might be ‘cup’ or ‘drink’.

Nevertheless, with the rise of large language and vision-language models, we now have capable segmentation models that do not directly predict semantic classes but use learned associations between language queries and classes to give us ’open-vocabulary’ segmentation [2]. Some models are especially powerful since they can be used with arbitrary language queries.

In this thesis, we aim to build on advances in 3D vision-based robot manipulation and large open-vocabulary vision models [2] to build a full pick-and-place pipeline for real-world manipulation. We also aim to find synergies between scene reconstruction and semantic segmentation to determine if knowing the object semantics can aid the reconstruction of the objects and, in turn, aid manipulation.

Highly motivated students can apply by sending an e-mail expressing their interest to Snehal Jauhri (email: [email protected]) or Ali Younes (email: [email protected]), attaching your letter of motivation and possibly your CV.

Topic in detail : Thesis_Doc.pdf

Requirements: Enthusiasm, ambition, and a curious mind go a long way. There will be ample supervision provided to help the student understand basic as well as advanced concepts. However, prior knowledge of computer vision, robotics, and Python programming would be a plus.

References: [1] Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, and R. Girshick, “Detectron2”, https://github.com/facebookresearch/detectron2 , 2019. [2] F. Liang, B. Wu, X. Dai, K. Li, Y. Zhao, H. Zhang, P. Zhang, P. Vajda, and D. Marculescu, “Open-vocabulary semantic segmentation with mask-adapted clip,” in CVPR, 2023, pp. 7061–7070, https://github.com/facebookresearch/ov-seg

Dynamic Tiles for Deep Reinforcement Learning

Linear approximators in Reinforcement Learning are well-studied and come with an in-depth theoretical analysis. However, linear methods require defining a set of features of the state to be used by the linear approximation. Unfortunately, the feature construction process is a particularly problematic and challenging task. Deep Reinforcement learning methods have been introduced to mitigate the feature construction problem: these methods do not require handcrafted features, as features are extracted automatically by the network during learning, using gradient descent techniques.

In simple reinforcement learning tasks, however, it is possible to use tile coding as features: Tiles are simply a convenient discretization of the state space that allows us to easily control the generalization capabilities of the linear approximator. The objective of this thesis is to design a novel algorithm for automatic feature extraction that generates a set of features similar to tile coding, but that can arbitrarily partition the state space and deal with arbitrary complex state space, such as images. The idea is to combine the feature extraction problem directly with Linear Reinforcement Learning methods, defining an algorithm that is able both to have the theoretical guarantees and good convergence properties of these methods and the flexibility of Deep Learning approaches.

Curriculum Vitae (CV);
A motivation letter explaining the reason for applying for this thesis and academic/career objectives.

Minimum knowledge

Good Python programming skills;
Basic knowledge of Reinforcement Learning.

Preferred knowledge

Knowledge of the PyTorch library;
Knowledge of the Atari environments (ale-py library).
Knowledge of the MushroomRL library.

Accepted candidate will

Define a generalization of tile coding working with an arbitrary input set (including images);
Design a learning algorithm to adapt the tiles using data of interaction with the environment;
Combine feature learning with standard linear methods for Reinforcement Learning;
Verify the novel methodology in simple continuous state and discrete actions environments;
(Optionally) Extend the experimental analysis to the Atari environment setting.

Deep Learning Meets Teleoperation: Constructing Learnable and Stable Inductive Guidance for Shared Control

This work considers policies as learnable inductive guidance for shared control. In particular, we use the class of Riemannian motion policies [3] and consider them as differentiable optimization layers [4]. We analyze (i) if RMPs can be pre-trained by learning from demonstrations [5] or reinforcement learning [6] given a specific context; (ii) and subsequently employed seamlessly for human-guided teleoperation thanks to their physically consistent properties, such as stability [3]. We believe this step eliminates the laborious process of constructing complex policies and leads to improved and generalizable shared control architectures.

Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] and [email protected] , attaching your letter of motivation and possibly your CV.

Experience with deep learning libraries (in particular Pytorch)
Knowledge in reinforcement learning and/or machine learning

References: [1] Niemeyer, Günter, et al. "Telerobotics." Springer handbook of robotics (2016); [2] Selvaggio, Mario, et al. "Autonomy in physical human-robot interaction: A brief survey." IEEE RAL (2021); [3] Cheng, Ching-An, et al. "RMP flow: A Computational Graph for Automatic Motion Policy Generation." Springer (2020); [4] Jaquier, Noémie, et al. "Learning to sequence and blend robot skills via differentiable optimization." IEEE RAL (2022); [5] Mukadam, Mustafa, et al. "Riemannian motion policy fusion through learnable lyapunov function reshaping." CoRL (2020); [6] Xie, Mandy, et al. "Neural geometric fabrics: Efficiently learning high-dimensional policies from demonstration." CoRL (2023).

Dynamic symphony: Seamless human-robot collaboration through hierarchical policy blending

This work focuses on arbitration between the user and assistive policy, i.e., shared autonomy. Various works allow the user to influence the dynamic behavior explicitly and, therefore, could not satisfy stability guarantees [3]. We pursue the idea of formulating arbitration as a trajectory-tracking problem that implicitly considers the user's desired behavior as an objective [4]. Therefore, we extend the work of Hansel et al. [5], who employed probabilistic inference for policy blending in robot motion control. The proposed method corresponds to a sampling-based online planner that superposes reactive policies given a predefined objective. This method enables the user to implicitly influence the behavior without injecting energy into the system, thus satisfying stability properties. We believe this step leads to an alternative view of shared autonomy with an improved and generalizable framework.

Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] or [email protected] , attaching your letter of motivation and possibly your CV.

References: [1] Niemeyer, Günter, et al. "Telerobotics." Springer handbook of robotics (2016); [2] Selvaggio, Mario, et al. "Autonomy in physical human-robot interaction: A brief survey." IEEE RAL (2021); [3] Dragan, Anca D., and Siddhartha S. Srinivasa. "A policy-blending formalism for shared control." IJRR (2013); [4] Javdani, Shervin, et al. "Shared autonomy via hindsight optimization for teleoperation and teaming." IJRR (2018); [5] Hansel, Kay, et al. "Hierarchical Policy Blending as Inference for Reactive Robot Control." IEEE ICRA (2023).

Feeling the Heat: Igniting Matches via Tactile Sensing and Human Demonstrations

In this thesis, we want to investigate the effectiveness of vision-based tactile sensors for solving dynamic tasks (igniting matches). Since the whole task is difficult to simulate, we directly collect real-world data to learn policies from the human demonstrations [2,3]. We believe that this work is an important step towards more advanced tactile skills.

Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] and [email protected] , attaching your letter of motivation and possibly your CV.

Good knowledge of Python
Prior experience with real robots and Linux is a plus

References: [1] https://www.youtube.com/watch?v=HH6QD0MgqDQ [2] Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction; Klas Kronander and Aude Billard. [3] https://www.youtube.com/watch?v=jAtNvfPrKH8

Inverse Reinforcement Learning for Neuromuscular Control of Humanoids

Within this thesis, the problems of learning from observations and efficient exploration in overactued systems should be addressed. Regarding the former, novel methods incorporating inverse dynamics models into the inverse reinforcement learning problem [1] should be adapted and applied. To address the problem of efficient exploration in overactuted systems, two approaches should be implemented and compared. The first approach uses a handcrafted action space, which disables and modulates actions in different phases of the gait based on biomechanics knowledge [2]. The second approach uses a stateful policy to incorporate an inductive bias into the policy [3]. The thesis will be supervised in conjunction with Guoping Zhao ( [email protected] ) from the locomotion lab.

Highly motivated students can apply by sending an e-mail expressing their interest to Firas Al-Hafez ( [email protected] ), attaching your letter of motivation and possibly your CV. Try to make clear why you would like to work on this topic, and why you would be the perfect candidate for the latter.

Required Qualification : 1. Strong Python programming skills 2. Knowledge in Reinforcement Learning 3. Interest in understanding human locomotion

Desired Qualification : 1. Hands-on experience on robotics-related RL projects 2. Prior experience with different simulators 3. Attendance of the lectures "Statistical Machine Learning", "Computational Engineering and Robotics" and/or "Reinforcement Learning: From Fundamentals to the Deep Approaches"

References: [1] Al-Hafez, F.; Tateo, D.; Arenz, O.; Zhao, G.; Peters, J. (2023). LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning, International Conference on Learning Representations (ICLR). [2] Ong CF; Geijtenbeek T.; Hicks JL; Delp SL (2019) Predicting gait adaptations due to ankle plantarflexor muscle weakness and contracture using physics-based musculoskeletal simulations. PLoS Computational Biology [3] Srouji, M.; Zhang, J:;Salakhutdinow, R. (2018) Structured Control Nets for Deep Reinforcement Learning, International Conference on Machine Learning (ICML)

Robotic Tactile Exploratory Procedures for Identifying Object Properties

Goals of the thesis

Literature review of robotic EPs for identifying object properties [2,3,4]
Develop and implement robotic EPs for a Digit tactile sensor
Compare performance of robotic EPs with human EPs

Desired Qualifications

Interested in working with real robotic systems
Python programming skills

Literature [1] Lederman and Klatzky, “Haptic perception: a tutorial” [2] Seminara et al., “Active Haptic Perception in Robots: A Review” [3] Chu et al., “Using robotic exploratory procedures to learn the meaning of haptic adjectives” [4] Kerzel et al., “Neuro-Robotic Haptic Object Classification by Active Exploration on a Novel Dataset”

Scaling learned, graph-based assembly policies

scaling our previous methods to incorporate mobile manipulators or the Kobo bi-manual manipulation platform. The increased workspace of both would allow for handling a wider range of objects
[2] has shown more powerful, yet, it includes running a MILP for every desired structure. Thus another idea could be to investigate approaches aiming to approximate this solution
adapting the methods to handle more irregular-shaped objects / investigate curriculum learning

Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] , attaching your letter of motivation and possibly your CV.

Experience with deep learning libraries (in particular Pytorch) is a plus
Experience with reinforcement learning / having taken Robot Learning is also a plus

References: [1] Learn2Assemble with Structured Representations and Search for Robotic Architectural Construction; Niklas Funk et al. [2] Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery; Niklas Funk et al. [3] Structured agents for physical construction; Victor Bapst et al.

Long-Horizon Manipulation Tasks from Visual Imitation Learning (LHMT-VIL): Algorithm

The proposed architecture can be broken down into the following sub-tasks: 1. Multi-object 6D pose estimation from video: Identify the object 6D poses in each video frame to generate the object trajectories 2. Action segmentation from video: Classify the action being performed in each video frame 3. High-level task representation learning: Learn the sequence of robotic movement primitives with the associated object poses such that the robot completes the demonstrated task 4. Low-level movement primitives: Create a database of low-level robotic movement primitives which can be sequenced to solve the long-horizon task

Desired Qualification: 1. Strong Python programming skills 2. Prior experience in Computer Vision and/or Robotics is preferred

Long-Horizon Manipulation Tasks from Visual Imitation Learning (LHMT-VIL): Dataset

During the project, we will create a large-scale dataset of videos of humans demonstrating industrial assembly sequences. The dataset will contain information of the 6D poses of the objects, the hand and body poses of the human, the action sequences among numerous other features. The dataset will be open-sourced to encourage further research on VIL.

[1] F. Sener, et al. "Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities". CVPR 2022. [2] P. Sharma, et al. "Multiple Interactions Made Easy (MIME) : Large Scale Demonstrations Data for Imitation." CoRL, 2018.

Adaptive Human-Robot Interactions with Human Trust Maximization

Good knowledge of Python and/or C++;
Good knowledge in Robotics and Machine Learning;
Good knowledge of Deep Learning frameworks, e.g, PyTorch;

References: [1] Xu, Anqi, and Gregory Dudek. "Optimo: Online probabilistic trust inference model for asymmetric human-robot collaborations." ACM/IEEE HRI, IEEE, 2015; [2] Kwon, Minae, et al. "When humans aren’t optimal: Robots that collaborate with risk-aware humans." ACM/IEEE HRI, IEEE, 2020; [3] Chen, Min, et al. "Planning with trust for human-robot collaboration." ACM/IEEE HRI, IEEE, 2018; [4] Poole, Ben et al. “On variational bounds of mutual information”. ICML, PMLR, 2019.

Causal inference of human behavior dynamics for physical Human-Robot Interactions

Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] , attaching your a letter of motivation and possibly your CV.

Good knowledge of Robotics;
Good knowledge of Deep Learning frameworks, e.g, PyTorch
Li, Q., Chalvatzaki, G., Peters, J., Wang, Y., Directed Acyclic Graph Neural Network for Human Motion Prediction, 2021 IEEE International Conference on Robotics and Automation (ICRA).
Löwe, S., Madras, D., Zemel, R. and Welling, M., 2020. Amortized causal discovery: Learning to infer causal graphs from time-series data. arXiv preprint arXiv:2006.10833.
Yang, W., Paxton, C., Mousavian, A., Chao, Y.W., Cakmak, M. and Fox, D., 2020. Reactive human-to-robot handovers of arbitrary objects. arXiv preprint arXiv:2011.08961.

Incorporating First and Second Order Mental Models for Human-Robot Cooperative Manipulation Under Partial Observability

Scope: Master Thesis Advisor: Dorothea Koert , Joni Pajarinen Added: 2021-06-08 Start: ASAP

The ability to model the beliefs and goals of a partner is an essential part of cooperative tasks. While humans develop theory of mind models for this aim already at a very early age [1] it is still an open question how to implement and make use of such models for cooperative robots [2,3,4]. In particular, in shared workspaces human robot collaboration could potentially profit from the use of such models e.g. if the robot can detect and react to planned human goals or a human's false beliefs during task execution. To make such robots a reality, the goal of this thesis is to investigate the use of first and second order mental models in a cooperative manipulation task under partial observability. Partially observable Markov decision processes (POMDPs) and interactive POMDPs (I-POMDPs) [5] define an optimal solution to the mental modeling task and may provide a solid theoretical basis for modelling. The thesis may also compare related approaches from the literature and setup an experimental design for evaluation with the bi-manual robot platform Kobo.

Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] attaching your CV and transcripts.

References:

Wimmer, H., & Perner, J. Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children's understanding of deception (1983)
Sandra Devin and Rachid Alami. An implemented theory of mind to improve human-robot shared plans execution (2016)
Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, SM Ali Eslami,and Matthew Botvinick. Machine theory of mind (2018)
Connor Brooks and Daniel Szafir. Building second-order mental models for human-robot interaction. (2019)
Prashant Doshi, Xia Qu, Adam Goodie, and Diana Young. Modeling recursive reasoning by humans using empirically informed interactive pomdps. (2010)

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Program’s objectives

In addition to classes spanning from electromechanical systems to advanced artificial intelligence, the program offers a large set of hands-on activities where students learn by designing, prototyping and validating robotic systems.

This program gives students a well-rounded education with practical experience, and leads to careers in a wide range of fields where robotics technologies are increasingly adopted, such as: biomedical technologies; logistics and transportation; aviation and drones; autonomous cars; industry 4.0; smart houses; environmental technology. In addition, students can benefit from EPFL’s strong innovation ecosystem to invent new systems and applications, and start up their own company.

Both core and optional classes include hands-on exercises aimed at applying theoretical aspects to real systems. In addition, for semester and interdisciplinary projects, as well as the final Master’s thesis, students work with researchers on challenging problems within EPFL robotics laboratories or in the industry.

Simplified study plan

Please note that the information regarding programs’ structure as well as the simplified study plans may be subject to change and that they are not legally binding. Only the official regulations and study plans are binding.

Directories

Graduate Program at Colorado School of Mines

Program overview.

4% projected job growth 2018-28

$75,487 average robotics engineering salary, glassdoor

Human-centered robotics lab

Requirements and Costs

Admission Requirements
Degree Requirements
Cost of Attendance

Graduate Certificate

Bachelor’s Degree : Required
GRE : Graduate Record Examination (Quantitative section) score of 151 or higher (or 650 on the old scale). Applicants who have graduated with a computer science, engineering, or math degree from Mines within the past five years are not required to submit GRE scores.
Letters of Recommendation : Not Required
Resume or Curriculum Vitae (CV) : Required
Statement of Purpose : Not Required. Suggested if GPA is less than 3.0/4.0
Transcript(s) : Required. Must be submitted for all schools attended (unofficial transcripts accepted for admissions review and must show successful completion of any required prerequisite course(s).

Master’s Non-Thesis

Bachelor’s Degree : Required
GRE : Graduate Record Examination (Quantitative section) score of 151 or higher (or 650 on the old scale). Applicants who have graduated with a computer science, engineering, or math degree from Mines within the past five years are not required to submit GRE scores.
Letters of Recommendation : Required – two letters. Letters of recommendation are not required for current Mines students.
Statement of Purpose : Required
For international applicants or applicants whose native language is not English, please review the ENGLISH PROFICIENCY requirement.

Master’s Thesis

Bachelor’s degree : Required
Letters of Recommendation : Required – three letters.

For additional information about these admissions requirements, please refer to the Admissions Requirements page

View the Mines Academic Catalog for more program-specific information

*Allowance for fees based on mandatory fees charged to all students. Does not include fees for orientation, library, yearbook, refrigerator rental, voice messaging, etc.

At less than 4.5 credit hours, you may be ineligible for financial aid.

Request for additional information

Fill out this form to receive more information about this exciting program.

Career Outcomes

Career Types
Career Resources
Robotics engineer
Aerospace engineer
Computer and information research scientist
Automated control system engineer
Development engineer

Career Services

Degree Options

Admissions deadlines.

Graduate Programs

Master of science in robotics.

One of the unique aspects of the MSR program at UD is in leveraging the campus-wide facilities, expertise, and existing faculty collaborations, to expose its students within the normal curriculum, to aspects of implementation and utilization of robotic systems in atmo/litho/hydro-spheres.

Students interested in our full-time campus experience are able to complete all degree requirements below, including the optional thesis, in as little as 18 to 24 months. See admission requirements and application information below.

Robotics is an interdisciplinary field that requires crossing the boundaries of traditional engineering disciplines. In the face of the rising expenditures on robotics, there will be a continuing and increasing need for skilled interdisciplinary engineers to design, build, and program robots in the future. This interdisciplinary Master of Science in Robotics (MSR) program is answering the societal, government, and industry need for specialized education in this field.

DEGREE REQUIREMENTS

The program builds on some of the unique strengths, resources, and expertise that can be identified across the University of Delaware campus, to offer a higher education and professional training opportunity that cannot be easily replicated.

Required Courses (18 credits)

Six (6) Required Courses: MEEG 621 Linear Systems CISC 621 Algorithm Design and Analysis MEEG 671 Introduction to Robotics MEEG 678 Introduction to Autonomous Driving CISC 642 Introduction to Computer Vision MAST 632 Environmental Field Robotics

Graduate-Level Engineering Electives (12 credits)

Four (4) graduate-level engineering electives are required. The following courses are pre-approved: CISC 681 Artificial Intelligence CISC 684 Introduction to Machine Learning MEEG 620 Intermediate Dynamics MEEG 677 Introduction to State Estimation MEEG 698 Stochastic Optimal Control MEEG 829 Applied Nonlinear Control MEEG 877 Sensing and Estimation in Robotics MEEG 890 Nonlinear Programming MEEG 894 Linear Feedback Control Design MEEG 895 Game Theory & Mechanism Design BMEG 441/667 Biomechatronics MEEG 467/667 Soft Robots: design, Principles and Applications

Graduate-level independent study can substitute for up to six (6) graduate elective credits along a non-thesis degree track. Independent study activities can take place outside the campus, in the context of semester-long internships with approved industry or government partners, but always under the supervision and oversight of a faculty member from the participating academic units, who will be ultimately responsible for assigning a grade for the course.

Thesis Option

A student pursuing the Thesis option can request to count six (6) credits of Master’s Thesis towards the required course credits.

Suggested Academic Concentrations (Optional)

We offer five (5) main concentrations, with the following suggested courses:

Control : MEEG 621 (Linear Systems); MEEG 698 (Stochastic Optimal Control) or MEEG 894 (Linear Feed-back Control Design); MEEG 829 (Applied Nonlinear Control)
Estimation : MEEG 621 (Linear Systems); MEEG 677 (Introduction to State Estimation); MEEG 877 (Sensing and Estimation in Robotics)
Artificial Intelligence : CISC 621 (Algorithm Design and Analysis); CISC 684 (Intro to Machine Learning); CISC 642 (Computer Vision)
Design : MEEG 620 (Dynamics); MEEG 671 (Intro to Robotics); MAST 632 (Environmental Field Robotics)
Optimization : CISC 621 (Algorithms); MEEG 895 (Game Theory and Mechanism Design); MEEG 890 (Nonlinear Programming)

“My graduate studies at UD gave me adequate knowledge to kick off my first job in building self-driving cars, and laid a good foundation for my later career.”

ADMISSION REQUIREMENTS

The requirements for admission to the MSR program are the following:

A baccalaureate degree in mechanical engineering or in a closely allied field of science or mathematics. Applicants with degrees in other disciplines may be admitted with provisional status and may be required to complete prerequisite courses that are deemed necessary for appropriate preparation for courses in the program.
An undergraduate grade point average in engineering, science and mathematics courses of at least 3.0 on a 4.0 scale.
The Graduate Record Examination (GRE) combined Quantitative and Verbal score of 308 (1200). Waivers may be considered on a case-by-case basis, with documented approval by the Department of Mechanical Engineering’s Admissions Committee.
International applicants: The TOEFL with a minimum of 100 on the IBT and a speaking score of 20. IELTS with a minimum score of 6.5 with no individual sub-score below 6.0 on the IELTS alternative.
Three letters of recommendation from former teachers or supervisors.
Statement of Purpose

All items should be uploaded into your graduate application ( https://grad-admissions.udel.edu/apply/ ). Admission is selective and competitive based on the number of well qualified applicants and the research opportunities available with the faculty. Meeting the stated minimum academic requirements does not guarantee admission.

Application Deadlines

MSR Fall Admission

January 31: Priority consideration for admission
July 31: Final deadline to apply

MSR Spring Admission

October 31: Priority consideration for admission
December 31: Final deadline to apply

Tuition rates of all programs can be seen on the Graduate Office’s Tuition webpage .

See official program policy statement for details.

For more information about this program, contact [email protected] or call (302) 831-2423.

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Doctoral Theses

Jacqueline M. Kory-Westlund. Relational AI: Creating Long-Term Interpersonal Interaction, Rapport, and Relationships with Social Robots , 2019. Ph.D. Media Arts and Sciences, MIT. [PDF]
Jin Joo Lee, A Bayesian Theory of Mind Approach to Nonverbal Communication for Human-Robot Interactions: A Computational Formulation of Intentional Inference and Belief Manipulation , 2017. Ph.D. Media Arts and Sciences, MIT.
Nicholas de Palma, Bidirectional gaze guiding and indexing in human-robot interaction through a situated architecture , 2017. Ph.D. Media Arts and Sciences, MIT.
Philip Robbel, Local Multiagent Control in Large Factored Planning Problems. September 2015. Ph.D. Media Arts and Sciences. MIT.
Sigurður Örn Adalgeirsson, Mind-Theoretic Planning for Social Robots. June 2014. Ph.D. Media Arts and Sciences, MIT. [PDF]
Adam Whiton, Sartorial Robotics: Electronic-textiles and fiber-electronics for social soft-architecture robotics. September 2013. Ph.D. Media Arts and Sciences. MIT.
Angela Chang, TinkRBooks: Tinkerable Story Elements for Emergent Literacy. August 2011. Ph.D. Media Arts and Sciences, MIT. [PDF]
Peter Schmitt, Original Machines: Developing CAD/CAM Tools for Object-Oriented Mechatronics. May 2011. Ph.D. Media Arts and Sciences, MIT. [PDF]
Jesse Gray, Reusing a Robot’s Behavioral Mechanisms to Model and Manipulate Human Mental States. June 2010. Ph.D. Media Arts and Sciences, MIT. [PDF]
Matthew Berlin, Understanding the Embodied Teacher: Learning for Sociable Robots. January 2008. Ph.D. Media Arts and Sciences, MIT. [PDF]
Cory Kidd, Designing for Long-Term Human-Robot Interaction and Application to Weight Loss . January 2008. Ph.D. Media Arts and Sciences, MIT. [PDF]
Guy Hoffman, Ensemble: Fluency and Embodiment for Robots Acting with Humans . September 2007. Ph.D. Media Arts and Sciences, MIT. [PDF]
Andrea Lockerd Thomaz, Socially Guided Machine Learning . May 2006. Ph.D. Media Arts and Sciences, MIT. [PDF]
Andrew Brooks, Coordinating Human-Robot Communication . November 2006. Ph.D. Electrical Engineering and Computer Science, MIT. [PDF]

Masters Theses

Daniella DiPaola, How does my robot know who I am?: Understanding the impact of education on child-robot relationships. , 2020. S.M. Media Arts and Sciences, MIT. [ PDF ]
Ravi Tejwani, Migratable AI., 2020. S. M. Media Arts and Sciences, MIT. [PDF]
Manushaqe Muco, Connecting symbols to primitive percepts using expectation as feedback, 2020. S. M. Media Arts and Sciences, MIT. [PDF]
Blakeley H. Payne, Can my algorithm be my opinion? An AI + ethics curriculum for middle school students, 2020. S. M. Media Arts and Sciences, MIT. [PDF]
Safinah Ali, Designing Child-Robot Interaction for Facilitating Creative Learning, 2019. S. M. Media Arts and Sciences, MIT. [PDF]
Ishaan Grover, A semantics based computational model for word learning, 2018. S. M. Media Arts and Sciences, MIT. [PDF]
Nikhita Singh, Talking machines: democratizing the design of voice-based agents for the home , 2018. S. M. Media Arts and Sciences, MIT. [PDF]
Pedro Reynolds-Cuéllar, The role of social robots in fostering human empathy : a cross-cultural exploration, 2018. S.M. Media Arts and Sciences, MIT. [PDF]
Huili Chen, Adaptive Role Switching in Socially Interactive Agents for Children’s Language Learning, 2018. S. M. Media Arts and Sciences, MIT.
Randi Williams, Leveraging Social Robots to Aid Preschool Children’s Artificial Intelligence Education, 2018. S. M. Media Arts and Sciences, MIT. [PDF]
Stefania, Druga, Growing Up With AI. Cognimates: from coding to teaching machines, 2018. S. M. Media Arts and Sciences, MIT. [PDF]
Sooyeon Jeong, The Impact of Social Robots on Young Patients’ Socio-emotional Well-being in a Pediatric Inpatient Care Context, 2017. S. M. Media Arts and Sciences, MIT. [PDF]
Samuel Spaulding, Developing affect-aware robot tutors, 2015. S. M. Media Arts and Sciences, MIT. [PDF]
David Nuñez, GlobalLit: a platform for collecting, analyzing, and reacting to children’s usage data on tablet computers. 2015. S. M. Media Arts and Sciences, MIT. [PDF]
Jacqueline Kory, Storytelling with robots: Effects of robot language level on children’s language learning , 2014. S. M. Media Arts and Sciences, MIT. [PDF]
Kristopher Dos Santos, The Huggable: A socially assistive robot for pediatric care. , 2012. S. M. Media Arts and Sciences, MIT. [PDF]
Natalie Freed, “This is the fluffy robot that only speaks french”: Language use between preschoolers, their families, and a social robot while sharing virtual toys. 2012. S. M. Media Arts and Sciences, MIT. [PDF]
Adam Setapen, Creating Robotic Characters for Long-Term Interaction. , 2012. S. M. Media Arts and Sciences, MIT. [PDF]
Kenton J. Williams, Physics-, Social-, and Capability-Based Reasoning for Robotic Manipulation. , 2012. S. M. Mechanical Engineering Department.,MIT. [PDF]
Jin Joo Lee, Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions. , 2011. S. M. Media Arts and Sciences, MIT. [PDF]
David Robert, Imaginative Play with Blended Reality Characters. , 2011. S. M. Media Arts and Sciences, MIT. [PDF]
Ryan Wistort, TofuDraw: Choreographing Robot Behavior through Digital Painting. ,2010. S.M. Thesis Media Arts and Sciences, MIT. [PDF]
Sigurður Örn Adalgeirsson. Mebot, a robotic platform for socially embodied telepresence. 2009. S.M. Thesis Media Arts and Sciences, MIT. [PDF]
Jun Ki Lee, Affordable Gesture Recognition Based Avatar Control System: A Puppeteering System for the Huggable , 2008. S.M. Media Arts and Sciences, MIT.
Mikey Siegel, Persuasive Robotics: Towards Understanding the Influence of a Mobile Humanoid Robot over Human Belief and Behavior . 2008. S. M. Media Arts and Sciences. MIT. [PDF]
Jeff Lieberman, Accelerated and Improved Motor Learning and Rehabilitation using Kinesthetic Feedback. 2006. S. M. Media Arts and Sciences, MIT. [PDF]
Walter Daniel Stiehl, Sensitive Skins and Somatic Processing for affective and Sociable Robots based upon a Somatic Alphabet Approach , 2005. S. M. Media Arts and Sciences, MIT. [PDF]
John McBean, Design and Control of a Voice Coil Actuated Arm for Human-Robot Interaction , 2004. S. M. Department of Mechanical Engineering, MIT. [PDF]
Jeff Lieberman, Teaching a Robot Manipulation Skills Through Demonstration, 2004. S. M. Department of Mechanical Engineering, MIT. [PDF]
Jesse Gray, Goal and Action Inference for Helpful Robots Using Self as Simulator , 2004. S. M. Media Arts and Sciences, MIT. [PDF]
Cory Kidd, Sociable Robots: The Role of Presence and Task in Human-Robot Interaction , June 2003. S. M. Media Arts and Sciences, MIT. [PDF]

M.Eng. Theses

Soo Jung Jang, Designing Parent-Child-Robot Triadic Storybook Reading Interaction. , 2021, M. Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [ PDF ]
Nada Hussein, Machine Audition Curriculum and Real-Time Music Accompaniment. , 2021, M. Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [ PDF ]
Matthew Huggins, Relational Dialogue., 2021, M. Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [ PDF ]
Hanna Lee, Interactive Storybooks with a Robot Companion. , 2018, M.Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [PDF]
Sooyeon Jeong, Developing a Social Robotic Companion for Pediatric Care for Stress and Anxiety Mitigation. , 2014, M.Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [PDF]
Nancy Foen, Exploring the human-car bond through an Affective Intelligent Driving Agent (AIDA). , February 2012, M.Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [PDF]
Julian Hernandez Munoz, Integrated Vision Framework for a Robotics Research and Development Platform. , May 2011, M.Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [PDF]
Maria Guirguis, Robot Search and Rescue: A Comparison of 3D Mapping Techniques. , May 2010, M.Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [PDF]
Heather Knight, An Architecture for Sensate Robots: Real Time Social-Gesture Recognition using a Full Body Array of Touch Sensors , September 2008, M.Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [PDF]
Collin Johnson, The Campus Tour Bot: A Robotic Tour Guide for MIT . September 2008. M.Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [PDF]
Robert Toscano, Building a Semi-Autonomous Sociable Robot Platform for Robust Interpersonal Telecommunication , May 2008, M.Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [PDF]
Andrew Wang, Physically Animated Desktop Computer for Ergonomic and Affective Movement , May 2006. M. Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [PDF]
Matt Hancher, A Motor Control Framework for Many-Axis Interactive Robots , June 2003. M.Eng. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. [PDF]

S.B. Theses

Kris Dos Santos, Project PEAC: A Personal, Expressive Avatar Controller for the Operation of Virtual Characters. , June 2010. Department of Mechanical Engineering, Massachusetts Institute of Technology. [PDF]
Yingdan Gu, Semi-Autonomous Mobile Phone Communciation Avatar for Enhanced Interaction. , February 2008. Department of Mechanical Engineering, Massachusetts Institute of Technology. [PDF]
Nicolina Akraboff, Design of Transmission Mechanisms for the Head of the ‘Huggable’ Robotic Teddy Bear. , January 2008. Department of Mechanical Engineering, Massachusetts Institute of Technology. [PDF]
Javier Matamoros, Design of a Mixed Reality Workspace for an Expressive Humanoid Robot. , June 2006. Department of Mechanical Engineering, Massachusetts Institute of Technology. [PDF]
Levi Lalla, A Design of Acutation Mechanisms for Use in the “Huggable” Robotic Teddy Bear. , June 2006. Department of Mechanical Engineering, Massachusetts Institute of Technology. [PDF]
Jeremy Scholz, Design of a Voice Coil Actuated Office Chair. , June 2004. Department of Mechanical Engineering, Massachusetts Institute of Technology. [PDF]
Dan Stiehl, Tactile Perception in Robots: From the Somatic Alphabet to the Realization of a Fully “Sensitive Skin” , June 2003. Department of Mechanical Engineering, Massachusetts Institute of Technology. [PDF]

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Master of Science (M.Sc.)

Robotics, Cognition, Intelligence

The master's program in Robotics, Cognition, Intelligence is unique in Germany. It combines various engineering disciplines, such as mechanical and electrical engineering, with informatics.

Course Homepage

4 (fulltime)

Winter semester: 01.02. – 31.05. Summer semester: 01.09. – 30.11.

Aptitude Assessment for Master
Possible for both winter and summer semester
Student Fees: 85.00 €
Tuition fees for international students

Information on Degree Program

Program profile.

Robotics is being transformed: whereas previously automatized industrial robots were principally used in production processes, today they have entered into many areas of life. They have left the factories and now encounter people in their everyday lives.

Whereas previously, specialists had to service them, today this can be done by anyone: thus robots do the vacuum cleaning at home or mow the lawn. Further still, people and robots cooperate more and more closely, without any walls between them. Tomorrow, robots will be both part of everyday life and of the human body. After all, today, wearable robotic exoskeletons already help paralyzed people to walk. As the distinctions between humans and machines blur, robotics developers are faced with new challenges. Robots must be able to react autonomously to unforeseen situations and to adapt. It is impossible to foresee every interaction and every action in advance, and to program accordingly. Therefore, through the use of artificial intelligence, robots too have to be able to learn.

The “Robotics, Cognition, Intelligence” master’s program is a joint program of the Departments of Informatics, Electrical Engineering, and Information Technology as well as Mechanical Engineering – it provides the basis upon which to participate in these fascinating developments.

As a graduate, you will have acquired a broad methodological and theoretical grasp of the foundations of robotics, cognition, and intelligent autonomous systems. In addition to informatics, you are also familiar with those aspects of electrical and mechanical engineering relevant to you.

You possess knowledge of classic robot control systems as well as of the areas of perception, image processing, and artificial intelligence. You are familiar with procedures relating to signal processing; sensory data analysis and fusion; and programming. You have a firm grasp of behavioral control as well as machine learning and human-robot interaction. Through cooperation with industry partners, you will have been able to gain your first experience of practice-led projects.

The broad spectrum of elective modules will have enabled you to create an individual competency profile.

Opportunities for graduates of the Robotics, Cognition, Intelligence master's program arise in research and industry. By way of example, career opportunities open up in aviation and aerospace, microelectronics, consumer electronics, biomedical engineering, or the automotive sector.

Program structure

The duration of the Robotics, Cognition, Intelligence master's program is four semesters.

The program offers a high degree of flexibility when it comes to selecting your own specialties. In addition to acquiring the theoretical and methodological foundations, elective modules in informatics as well as mechanical and electronic engineering allow you to pursue your own choice of specialties.

The degree concludes with the master‘s thesis, in which you apply acquired knowledge and skills in a practice-oriented project.

Language of instruction

Required language skills for admission:

You need sufficient German and English language skills if you wish to apply for this program. Learn more about recognized certificates and other ways to prove your language skills.

This evidence of your language proficiency confirms that you comply with the minimum language requirements for admission to the program. Depending on the program and your individual background, it may be necessary for you to keep working on your language skills during your studies. Be sure to take a look at the services of our Language Center.

Languages of instruction:

The languages of instruction for this program are German and English. This means that you need to complete modules in both German and English in the course of your studies.

Information on study organization

Information on exams
Information on studying abroad

Fees for the program

The tuition fees for international students from third countries for this degree program are 6,000 euros per semester .

Many international students can have their fees waived or receive scholarships to finance them. You can find all information on waivers and scholarships here.

Please note: The semester fee as a contribution to the student union must be paid additionally. It varies depending on where you are studying. You can find all information on the semester fee here.

Academic Regulations: Application, Studying and Exams

General Academic and Examination Regulations
Academic and Examination Regulations (PDF 404 KB)
All regulations and legal framework concerning studies

Application and Admission

Application process.

Minimum requirements to apply for a Master's program at TUM are a recognized undergraduate degree (e.g. a bachelor’s) and the successful completion of the aptitude assessment procedure. Aptitude assessment allows the TUM school or department to which you are applying the opportunity to evaluate your individual talents and motivation for study.

During the application period, you must apply through the TUMonline application portal and upload your application documents.

If you receive an offer of admission, you will additionally have to submit individual documents as notarized hardcopies by post to be enrolled.

Generally, applicants with a qualification for postgraduate studies (e.g. a bachelor’s) obtained outside of the EU / EEA must have their documents reviewed in advance through uni-assist.

Applying for a master’s program: Application, admission requirements and more
Important information about your application from the TUM school or department

Documents required for the online application

Degree Certificate and Diploma or Subject and Grade Transcript of Studies to Date
Transcript of Records
Proof of German Language Proficiency
Proof of English Language Proficiency
Curricular Analysis
Letter of Motivation
Complete and Current Résumé
Preliminary Documentation (VPD) if the qualification for graduate studies (e.g. a bachelor's) is obtained outside the EU/EEA

We may require additional documents depending on your educational background and your country of origin . Complete the online application to receive a comprehensive list of the required documents.

Documents required for enrollment

Application for Enrollment (signed)
Degree Certificate and Diploma (certified copy)
Transcript of Records (certified copy)
Most Current Photo (as for ID)
Digital notification of your health insurance status from a German public health insurance provider (requested by applicant)

We may require additional documents depending on the type of educational background you earned and your country of origin . After accepting an offer of admission in TUMonline, you will receive a list of documents you must submit to TUM in hardcopy for enrollment.

Application deadlines

Application period for winter semester: 01.02. – 31.05. Application period for summer semester: 01.09. – 30.11.

During the application period, you must apply through the TUMonline application portal and upload your application documents. Please be aware that we can only process your application if you upload all required documents within the application period .

We will review your application as soon as it is complete. Please check your TUMonline account regularly, to see if we have any queries to your documents or if you have to amend one or more documents.

After receiving admission, you will see in TUMonline which documents you have to submit for enrollment , and in which form. Please note that you always have to send the signed application for enrollment and all notarized hardcopies by post .

We recommend that you submit the documents for enrollment as soon as possible after receiving admission. If individual documents are not available by then, you can submit them up to 5 weeks after the start of the lecture period. You will, however, only be enrolled once we have received all documents.

You can check the status of your application at any time in your TUMonline account .

Admission process

Selection takes place through an aptitude assessment procedure. Aptitude assessment is a two-part procedure after the submission of an official application to a program. In this procedure, the TUM school or department determines whether you meet the specific requirements for its master’s degree program.

In the initial stages , the grades you obtained during your bachelor's program, as well as your written documents, will be evaluated using a point system . Depending on the amount of points accumulated , applicants are either immediately admitted , rejected or invited to an admissions interview .

Description of the Aptitude Assessment (Appendix 2, German) (PDF 333 KB)

TUM School of Computation, Information and Technology

General student advising & student information.

Questions about application and admission

Contact Hours

General Student Advising

Appointments by arrangement in advance

Departmental Student Advising

Admission and application

Online application

Barrier-free education.

MSc Robotics

In the MSc Robotics program, you will acquire the expertise necessary for the development of future robotics systems. The program positions you at the forefront of the intersection between robotics and AI, with a primary focus on the creation of intelligent interactive robotic systems, as opposed to traditional "mindless" robots which can only perform repetitive tasks. This emphasis encompasses a wide array of applications, including self-driving vehicles, robots that learn from experience, human-robot interfaces, mobile manipulators and tactile technology, research on human comfort and acceptance, rehabilitation robotics, and agricultural robots, among others. By working with and learning from experts in the field, you will become proficient in researching, developing, implementing, and testing novel AI solutions that will be commonplace in future mechanical systems. The MSc Robotics curriculum encompasses courses on a wide range of fundamental theories, techniques, and practical skills while also offering opportunities for specialization in your preferred areas. Given the inherently creative and interdisciplinary nature of robotics, this broad foundation will ensure your comprehensive understanding of all aspects of future smart robot development, enabling you to understand the impact of your work on the full robotic system, and facilitating effective communication with fellow team members. Through specialization, you will cultivate a distinct professional profile and acquire the profound knowledge necessary to address the ever-evolving challenges within the field of robotics. The hosting department, Cognitive Robotics (CoR), envisions a future in which robotics engineers play a pivotal role in steering society towards greater reliance on robotics. This vision highlights the significance of our program's holistic approach, which places importance on technical expertise, but also on self-development and ethical awareness. As the world rapidly changes, personal and leadership development becomes increasingly vital, enabling students to assume control of their professional decisions and cultivate critical thinking skills across diverse cultural and societal contexts.

Introduction Week

Information.

Information concerning the introduction program will be sent 2-3 weeks before the academic year starts.

The first week of September there is a compulsory introduction program for new students and February starters. During this week we introduce the curriculum, explain the organization and procedures. We also organize some nice activities and workshops.

The first year of the programme consists of nine obligatory courses for all Robotics students, with a total of 40 EC. The goal of these courses is to give all students a solid common background in Robotics. The courses are: (1) Dynamics and Control (2) Machine Learning (3) Robot Software Practicals (4) Machine Perception (5) Planning and Decision Making (6) Human-Robot Interaction (7) Robots and Society (8) Multidisciplinary Project* (9) Vision and Reflection A more detailed description of the curriculum can be found in the programme overview , and detailed course information is listed in the digital study guide .

Second Year

During the second year, you first obtain 15 EC by either completing an internship, research assignment, or by following additional courses. Finally, under supervision of a staff member of the Cognitive Robotics (CoR) department, you will perform a literature study (10 EC) and complete your MSc thesis project (35 EC). The thesis project may be done at a company, if your CoR thesis supervisor approves the topic.

As a MSc student, you are expected to take the lead in arranging these projects yourself and approach the researchers in the Cognitive Robotics department that you would like to be your supervisor.

Master Assignments

Get inspired and have a look at the department's website

Additional Information

Study association, brightspace, contact information.

Coordinator-COR-ME@tudelft.nl

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MSc-Thesis Project

Description.

The MSc-Thesis Project (MTP) is the final individual research project of the MSc Robotics programme of 40 EC. It is recommended to perform the MSc-thesis project at one of the research groups of the UT, but it can be done outside the UT. This is only allowed if no internship outside the UT has been performed, so variant 3 of Year 2. Note that in all cases, the research group is in the lead of drawing up the project topic.

After the MSc-Thesis project has been completed, the student is able to: • apply a suitable research or design methodology in a scientific manner. • deliver an original contribution to the research group. • run a scientific project within its time frame. • write a scientific report and hold a public presentation. • communicate to peers and non-specialists.

The MSc-Thesis Project is about further specialising in the direction of the expertise of the robotics research group of the UT where this project is performed. Usually, the MSc-Thesis Project is part of ongoing research at the research group so the research group is in the lead of drawing up the project topic. An Internship is about gaining work experience on an academic level of course by conducting a small project outside UT. This makes the MSc-Thesis Project and Internship quite distinct from each other: only running a project on a robotics topic is similar.

The MSc-Thesis Project is carried out within a robotics-related research group of the UT. The list of recognised robotics-related research groups is indicated in the table. <to be added> This list is also in the EER-B, Article B4.7.2, Table 16.

The topic of the MSc-Thesis Project must be in the scientific fields on which the Specialisation of the student is grounded. Furthermore the MSc-Thesis project must address aspects being taught in the Profile of the student.

CBL-like techniques can be applied, as the MSc-Thesis Project is formulated as a rather open problem. In fact, MSc-Thesis Projects are CBL projects avant-la-lettre, as conducting thesis projects has its origin far earlier then CBL way of working.

Students must report on CBL activities applied during the MSc-Thesis Project in their portfolio of the course CBL in MSc Robotics 2 ( 202200121 ), for which you must enrol (open all year). As usual for CBL work, the CBL contribution to the portfolio is assessed separately from the MSc-Thesis Project.

Note that this CBL assessment (and finishing) is done in the beginning of the third phase of the Thesis work, in order not to interfere with the finalisation of the MSc-Thesis Project.

There is no Canvas page of the MTP, as there is no synchonised start each year, the work is in the research groups, and the forms needed for doing the MTP are available on this website: the Forms and Procedures Page .

Entry Requirements

To start your MSc-Thesis project (MTP), a maximum of 10 EC next to the MSc-Thesis Project may be open, whereby the six compulsory courses of the chosen specalisation and CBL Year 1 ( 202200115 ) must have completed. See also EER, Article B3.12. In case of variant 2 of Year 2, so taking the Academic-Skills Project ( 202200119 ), this project must also have been completed before starting the MSc-Thesis Project.

You must satisfy these requirements at the moment you actually start the MSc-Thesis Project. You can start acquiring an MSc-Thesis Project before you meet these entry requirements, as it takes a month or two to acquire an MSc-Thesis Project.

You may start the MSc-Thesis project at a moment that results of examinations are still pending. However, in case these results appear to be insufficient later, the Examination Board may order you to interrupt your MSc-Thesis Project to repair these insufficient results.

The study load of an MSc-Thesis Project is 40 EC, being 28 weeks of full-time work. Any day off, public holiday, or time spend on other courses or jobs extends the wall-clock time (calendar time) of this period. This 28 full-time weeks is the minimal duration of an MSc-Thesis Project project. You have to set up a planning, in which all extra days are taken into account, such that netto 28 full-time working weeks are mapped onto Calendar days. As some time is consumed due to organisational issues and slack is inevitble, 4 weeks are added to compensate for this. It is advised to spend at least 60-80% of full time to your MSc-Thesis project to keep the project going and not to lose too much time to get started again and again.

Regulations in force

The regulations to which the MSc-Thesis project must comply are in the Education and Examination Regulations ( EER ), most notably Articles A3.7, A3.8, B3.12, B4.7, and B4.8. The text here is based on those regulations. In case of discrepancy between this website text and the EER, the EER is leading.

Acquiring an MSc-Thesis Project

As a student, you must find an MSc-Thesis Project yourself. Robotic research groups often advertise student projects on their website or provide information on research projects they conduct. Together with the envisaged supervisor(s) an MSc-Thesis Project is defined, either (partly) by the student or by the supervisor(s). This holds for both an internal-at-UT project as for an external-outside-UT project. The research group, however, is in the lead of drawing up the topic of the MSc-Thesis Project in all cases.

The supervisor(s) check explicitly whether the proposed MSc-Thesis-Project idea is doable by the student, with respect to time budget, expected knowledge and skills, academic level, and whether it matches with the specialisation and the profile the student has chosen.

The MSc-Thesis Supervision Committee consists of at least a senior examiner chairing this committee, and a day-to-day supervisor. These two roles may be combined in one person. The MSc-Thesis Assessment Committee is the MSc-Thesis Supervision committee plus the external examiner, see EER-B Article B4.7. The supervisors are responsible for the composition of both the Supervision Committee and the Assessment Committee.

It is important to start in time preparing for the MSc-Thesis-Project, as consulting scientific staff and asking them about an MSc-Thesis-Project, takes time. We advise you to start looking for an MSc-Thesis-Project about two months before the expected start of it.

As student, you must register your MSc-Thesis-Project in the Mobility Online system, and send the MSc-Thesis Project Registration Form (also signed by the chairperson of your Supervision Committee) to the administration. The Examination Board checks composition of the Supervision Committee and the topic of the MSc-Thesis-Project using this information.

Running the MSc-Thesis Project

Use the Doing Projects approach: splitting project time into three equal parts: exploration, production, finalisation, with a project plan after 1/3, a ‘demo’ after 2/3, and a report and presentation at the end (obviously).

More details are in the DoingProjects document (also on the MSc-Robotics Canvas site, as this document is not yet finished), and an overview is below.

Starting and Exploration Phase

At the beginning of the project, make agreements concerning:

Milestones of 1/3, 2/3 and final date These dates mark the ends of the Exploration Phase, Production Phase, and Finalisation Phase, respectively. This implies a meeting in which the Project Plan respectively Demo are discussed, and the final presentation and thus assessment is held. These dates must be put on your MSc-Thesis Project Registration Form such that these are known by the administration. Note that the planning of these milestones is the initial planning, which may be updated during the course of the project. Obviously, updates must be send to the administration, see below at the "Feedback, Assessment, Extension" subsection.
Practical issues Like workplace, access to Lab, where to go for support, use of tools and servers etc. This is of course specific to the research group where the work is conducted.
Weekly PIP meetings On Progress, Issues, Plans: once per week with the student and at least day-to-day supervisor.
Monthy PIPPF meetings Monthly meetings on Progress, Issues, Plans, and next to that, Planning and Formal Formative Feedback: once per month with Supervision Committee and student. It is about PIP on project level, and on the global planning of the whole project. Furthermore, the Supervision Committee gives formative feedback to the student. See EER Article B4.7, Paragraphs 6 - 10.

When the work is done part-time, this rhythm can be scaled accordingly. It is advised to spend at least 80% of full time to your MSc-Thesis Project to keep the project going and not to lose too much time to get started again and again.

Note that the planning of these milestones at the start of the project is the initial planning and thus provisional. These may be updated during the course of the project.

The result of the Exploration Phase is the Project Plan, covering the following topics:

Introduction to the project Context, problem statement, goals of the project. The goals can be formulated as research questions or design objectives, depending on the nature of the project.
Analysis / Feasibility Literature review, analysis of the problem resulting in requirements (for design work) / how to proceed; test experiments to support feasibility reasoning. Especially for design-oriented projects, this includes possible approaches with advantages / disadvantages, presented in Design-Space Exploration tables, for example.
What to show at Demo Meeting Describe the testing (verification / validation approach), especially what is needed to get out of tests, to support the scientific reasoning, and as such goes into the final report. Describe what to show at the Demo milestone. And thus, to know what to work for in the Production Phase.
Initial Structure of the final report To give a target to work towards, and pinpoints to what must be delivered in the final report.
Action plan Tasks and the planning of the work to be done in the production phase.

At the end of this Exploration Phase, during that PIPPF meeting, the Project Plan is discussed, and formal formative feedback is given. The date of this meeting is reported to the administration by the supervisor.

If the Supervision Committee expects no pass to be achieved at the planned end date, repair actions can be set up, ranging from updating the project content, changing / extending supervision, adapting student’s way of working. If at a next PIPPF the performance of the students is still below par, the resit policy can be started: project time is extended with 2 months, and the grade is maximally a 6 for a Pass or a Fail.

Production Phase

This is executing the project plan and document the progress (logbook like) as that contributes to the final report.

In these kind of scientific projects, often work appears to be different than originally planned. This is due to growing insight, growing experience, unforeseen issues popping up, etc. To keep on track tasks, priorities of tasks, and thus planning need to be reconsidered and updated when necessary. This is part of the doing projects activity. Decisions on changing the plan must be taken together with the supervisors.

Especially at PIPPF meetings, the plan and progress of the project as a whole is discussed, and the Supervision Committee gives formative feedback. If necessary, repair actions can be started, or the resit policy can be set up. See EER Article B4.7, especially Paragraphs 6 – 10.

The Demo (showing essential results) at the end of the production phase, needs to be carefully prepared. This demo is a kind of (design) review to discuss and gather feedback from the supervisors. Next to that, it is to become clear and to decide that the work is good enough to enter the Finalising Phase. Often, many new ideas arise, so the left-over work and new things must be prioritised to avoid overloading and thus unnecessary extending the Finalisation Phase. The date of this demo meeting is reported to the administration by the supervisor.

Finalising Phase

First is to update / detail out the planning of this phase: plan the left-over work agreed to be done at the demo meeting, and plan the report writing, taking into account feedback moments and reading time for reviewing by supervisors.

On report writing: discuss the articulated outline first ( rich report outline ), that is a global line of thought of the report, so more then only chapter and section headings. For the review process, check the process as is used at the research group. Often, the day-to-day supervisor reviews draft-thesis chapters in between. Use earlier made documentation, including material of the project plan, obviously.

At least four weeks before the presentation day, the so-called green-light meeting is held, in which the draft report is discussed with / scrutinised by the Supervision Committee. For this, the report must be submitted one week earlier, so five weeks before the presentation day. The Supervision Committee decides on green light, that is, the work and draft report are good enough to give a final presentation and can be assessed after the presentation, provided the work to be done is of same quality as shown before. The senior examiner co-signs the Master-Examination Application Form, and the student sends it to the administration. This form must be at the administration at least four weeks ahead of the presentation day (the administration needs some time to process all checks, etc).

The green-light meeting can best be planned at the first PIPPF meeting after the Demo meeting (so one month after the Demo meeting). Also, the presentation day can be decided upon. The Supervision Committee can start looking for the external examiner, or wait for the green-light decision to do this.

The presentation takes 30 minutes, and after that, a Q&A session of about 30 minutes is held. This is in public at the University of Twente.

After presentation and assessment, deliver all the artifacts (including documentation and data) according to the process as used at the research group, and clean up lab space if applicable. This must be done within one week after the presentation and assessment.

Next to the report on the content of the work, as student, you must write and submit on CBL activities and add that to the CBL portfolio of the course CBL in MSc Robotics 2 ( 202200121 ). Best is to submit this report in the beginning of the Finalising Phase. As usual for CBL work, this CBL contribution to the portfolio is assessed separately from the MSc-Thesis Project.

Feedback, Assessment, Extension

Feedback during the project is embedded in the workflow of the MSc-Thesis Project (See EER Article 4.7):

During each PIPPF meeting, the supervision committee give a formal formative assessment on the process including planning and content. This feedback is related to the assessment criteria as presented on the assessment form.
This formative feedback can trigger the repair policy : in case it turns out that the original project appears to be too much / too complex for MSc-thesis work or equipment / tools are not functioning as expected, the project can be updated w.r.t. topics and/or planning, arrangement of extra technical support, adaptation of supervision approach / policy, etc.
If at the next formative feedback (the next PIPPF meeting), so, after working a month according to this repair policy, the student’s work is not back on track (that is, the student’s work is still not good enough to achieve a pass at the very end of the MSc-thesis project), the resit policy can be started. In case the level of results are beyond the control of the student, the repair policy can be reviewed and adapted, which may cause a delay of the project.
Resit policy implies that the student can extend the project with a maximum of 9 full-time weeks (2 months). Assessment of this extended MSc-thesis project results in a pass with grade of 6 or a fail. This implies that the resit policy can already be started before the summative assessment at the 'normal' end of the project.
In case the repair policy implies the project gets delayed (so due to reasons beyond the control of the student), the planning must be updated accordingly, and agreed upon as such by the examiner. In case no agreement on the rescheduling of the plan is reached, the student can request the EB to mediate.
In case of a delay due to the repair policy, or if the resit policy is started, the student must send the updated and signed registration form to the student administration (BOZ), acting as the registrar of the EB.

Final summative assessment of the MSc-thesis project is done by at least two examiners, one being responsible for the day-to-day supervision and the other being an independent colleague from outside the research group of the supervisor, who is not involved in the supervision. Criteria for grading, including rubrics, are in the assessment form . When at assessment it turns out that the work is insufficient, the resit policy can be started. In case the assesment is still a Fail after the resit, the student has to look for another MSc-thesis project.

Within one week after the presentation and assessment, the student must have submitted all documentation and data to the supervisors. If applicable, decide on reuse or disassemble the setup that has been developed / used, and further clear the lab space being used.

For the Supervisors

Supervising as examiner / senior examiner an MSc-Thesis project of MSc Robotics implies

Check and approve explicitly whether the proposed MSc-Thesis-Project idea is doable by the student, with respect to time budget, expected knowledge and skills, academic level, and whether it matches with the specialisation and the profile the student has chosen. This approval must be done before the student starts their MSc-Thesis-Project, and indicate that on the MSc-Thesis Registration Form.
As senior examiner, co-sign the the MSc-Thesis Registration Form, and Mobility Online form of the student. All forms used for the MTP are on the Forms and Procedures page .
Act as a supervisor and arrange day-to-day supervision, that is, compose the Supervision Committee. Use the DoingProjects supervision scheme for the actual supervision.
Provide formal formative feedback at each PIPPF meeting. The PIPPF meetings at the end of the Exploration Phase and Production Phase, deal with reviewing the project plan and demo respectively. Inform the administration (BOZ) by sending the dates of these meetings to BOZ.
If needed, formulate repair actions, or start the resit policy. See EER Article B4.7. In case these change in plans cause delay (always so in case of the resit policy), sign the updated MSc-Thesis Registration Form (signature of one of the supervisors suffices), and let the student send it as usual to BOZ.
Green-light meeting: Review / scrutinise the draft report, consolidate the presentation date, and arrange the external examiner. As senior examiner, co-sign the the master-examination application form of the student.
After presentation and Q&A session, and based on the work and report, assess the project by the Assessment Committee. Use the specific assessment form to record and file the assessment. An editable and prefilled form will be sent to you by BOZ shortly before the assessement day. Also at this moment, the resit policy can be applied if need. However, try to avoid applying the resit policy so late in the process. The resit policy implies 9 full-time weeks (2 months) extra time and grading with a 6 in case of a Pass, or a Fail. See EER Article B4.7 Paragraphs 10 and 15.
Send the filled-in and signed assessment form to the administration, often via your secretariat, after the student has submitted all data / documents / artefacts, but not later than one week after the presentation and assessment.

Exciting news for all prospective students

RWTH German Engineering College as a substitute for the GRE

RWTH International Academy
Master's Degree Programs

M.Sc. Robotic Systems Engineering

Become a robotic engineer and co-create the “Industry 4.0” by building intelligent robotic systems where humans and robots collaborate. In our M.Sc. Robotic Systems Engineering (M.Sc. RoboSys) program you will utilize your knowledge from various scientific disciplines to develop, implement and program cyber-physical systems and smart robots.

Take your engineering skills to the next level

The M.Sc. RoboSys focuses on the optimal use of robots in industry and society as well as the development and construction of novel robotic systems. Bridging gaps between mechanical engineering, electrical engineering and computer science, our program will enable you to develop innovative and intelligent robotic solutions to address the most pressing global challenges of the 21 st century: industrial productivity, energy efficiency, environmental responsibility, healthcare, and mobility services.

As an M.Sc. RoboSys student, you will learn to

create industrial robots, mobile robots, assistance robotics and intralogistics robotic systems
develop, implement and program robotic systems for different levels of autonomy
design automated and programmable robots that can operate in an agile environment
develop robotic systems for embedded production systems or autonomous warehouses

The RoboSys program is under the academic direction of the Institute of Mechanism Theory, Machine Dynamics and Robotics (IGMR) of RWTH Aachen University. In teaching and research, the IGMR focuses on mechanism theory and kinematics, machine dynamics and vibration technology, and robotics and mechatronics. By applying their expertise of mechanics, electrical drives, sensor technology and information processing you will develop robotic systems that help shape the digital transformation in industry and society.

Get an idea of the program

The M.Sc. RoboSys comprises three semesters of lectures, exercises and practical courses. In the fourth semester, you will not only write your Master's thesis, but will also have the chance to either focus on practical or academic experience according to your individual career aspiration:

An Internship is the right choice for you, if you want to

gain practical work experience during your studies
write a practice-oriented Master’s thesis
pursue a career in industry

A Research Project is the right choice for you, if you want to

get involved in intensive research work
write a research-oriented Master’s thesis
aim for a PhD after your master degree

Check out the curriculum and the detailed module manual for M.Sc. RoboSys . Downloaded from RWTHonline on September 23, 2022, subject to change without notice, all data without guarantee.

Compulsory Courses

Robotic Systems
Advanced Robotic Kinematics and Dynamics
Control Engineering
Machine Learning
Computer Science in Mechanical Engineering II
Multibody Dynamics
Computer Vision I
Robotic Sensor systems
Simulation of Robotic Systems, Sensors and Environment
The 12-week internship gives you the opportunity to gain further practical experience in the industry

Language Courses

We strongly recommend learning German. Learning German is essential to successfully integrate into a German cultural environment, but especially to pursue internship and job opportunities in Germany.

Elective Courses

Production Metrology
Machine Dynamics of Rigid Systems
Industrial Logistics
Artificial Intelligence and Data Analytics for Engineers
International Factory Planning
Advanced Electrical Drives
Summer School: Advanced Topics in Robotic Systems Engineering
Advanced Machine Learning
Introduction to Artificial Intelligence
Power Electronics
Processes and Principles for Lightweight Design
Applied Numerical Optimization Engineering
Numerical Methods in Mechanical Engineering
Advanced Finite Element Methods for Engineers
Strategic Technology Management
Mechatronics and Control Techniques for Production Plants
Advanced Control Systems

Research Project

The Research Project allows you to participate in current research at RWTH and write your first small research paper.

Master Thesis

The Master’s thesis is the final examination to complete the M.Sc. RoboSys. The Master’s Thesis can be written internally with an Institute or externally with a company.

Explore your future career

Increasing automation and digitalization are having an enormous impact on global competition. Using robotic systems is essential to increase industrial productivity while ensuring high-quality and cost-effective production. As a result, the industry is more than ever seeking robotics specialists with interdisciplinary knowledge in design, manufacturing, production, planning and management. The M.Sc. RoboSys responds to these developments with an application-oriented curriculum and the most up-to date technology knowledge, thus training the experts of tomorrow.

Upon successful completion of the M.Sc. RoboSys, you

understand the optimal use of robots in production
know how to combine and apply technological skills of mechanics, electrical drives, sensor technology, and information processing to optimize robotic systems
have the skills to organize and monitor operational and manufacturing processes
can identify future issues, technologies and developments in the field of robotics

Find out what fields our M.Sc. RoboSys graduates work in …

… and what careers they pursue

Your path to success

We want you to be successful - right from the start! Even before you begin your studies, we plan your course schedule with you and agree on goals that you want to achieve in each semester. Regular meetings and proactive academic advising during your time with us will help you identify your strengths and continually develop them. Our comprehensive career program and intensive individual advice and support, will equip you to graduate with honors and give you a head start for a unique career in Germany, Europe or worldwide.

Your way to us

Do you want to take your career to the next level with a Master's Degree at RWTH Aachen University? Start preparing your application now and discover how you can experience a successful start at RWTH Aachen University.

To become a RoboSys student you must fulfill the following admission requirements:

1. A Bachelor Degree

Bachelor of Engineering, Bachelor of Technology or Bachelor of Science in mechanical engineering or a related discipline (e.g. automotive, aerospace, energy, manufacturing, structural, industrial, or production engineering)

2. Relevant Work Experience

Our Master’s programs require a minimum of 12 months relevant work experience by the time of enrollment. At the time of application, you must provide proof of at least 6 months work experience.

3. GRE General Test or Completed GEC

Applicants must provide the GRE General Test. These minimum scores must be achieved: Verbal Reasoning: 145 points Quantitative Reasoning: 160 points Analytical Writing: 3 points

GRE Exemption: Applicants who are graduates of our German Engineering College (GEC) are exempt from the GRE.

4. Fundamental knowledge in the following subjects

Engineering (60 ECTS)	Mathematics and natural sciences (25 ECTS)	System sciences (16 ECTS)

Application Period

Non-EU applicants: December 4 to March 1 (Applicants who are citizens of a country outside of EU/EEA)
EU applicants: December 4 to July 15 (Applicants who are citizens of a EU/EEA country, applicants who are currently enrolled at RWTH Aachen and applicants who are degree holders of a German University)

Any further questions?

We will be happy to answer your questions about the curriculum, our admission requirements, our scholarship opportunities, and whatever else is on your mind. Just write us an e-mail and we will get in touch with you.

Book a digital one-on-one appointment

Would you like to speak with our Student Advisor directly and benefit from a personal consultation?

Please book your personal one-on-one appointment anytime and we will assist and guide you the best we can.

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Princeton University Library

Master's thesis and phd dissertation submission guidelines.

The Princeton University Archives at the Mudd Manuscript Library is the repository for Ph.D. dissertations and Master’s theses. The Princeton University Archives partners with ProQuest to publish and distribute Princeton University dissertations beyond the campus community.

Below you will find instructions on the submission process and the formatting requirements for your Ph.D. dissertation or Master's thesis. If you have questions about this process, please use our Ask Us form or visit the Mudd Manuscript Library during our open hours.

Ph.D Dissertation Submission Process

The first step is for the student to prepare their dissertation according to the Dissertation Formatting Requirements . Near the time of the final public oral examination (FPO) (shortly before or immediately after) the student must complete the online submission of their dissertation via the ProQuest UMI ETD Administrator website . Students are required to upload a PDF of their dissertation, choose publishing options, enter subject categories and keywords, and make payment to ProQuest (if fees apply). This step will take roughly 20-25 minutes.

After the FPO the student should log on to TigerHub and complete the checkout process. When this step is complete, Mudd Library will be notified for processing. This step will occur M-F during business hours. The Mudd Library staff member will review, apply the embargo (when applicable), and approve the dissertation submission in ProQuest. You will receive an email notification of the approval from ProQuest when it has been approved or needs revisions.

The vast majority of students will not be required to submit a bound copy of their dissertation to the library. Only students who have removed content from the PDF to avoid copyright infringement are required to submit a bound copy to the library. This unredacted, bound version of the dissertation must be formatted according to the Dissertation Formatting Requirements , and delivered by hand, mail, or delivery service to the Mudd Manuscript Library by the degree date deadline in order to be placed on the degree list. Address the bound copy to: Attn: Dissertations, Mudd Manuscript Library, 65 Olden Street, Princeton, NJ 08540.

ProQuest Publishing Options

When you submit your dissertation to the ProQuest ETD Administrator site, you will be given two options: Traditional Publishing or Open Access Publishing Plus. ProQuest compares the two options in their Open Access Overview document . Full details will be presented in the ProQuest ETD Administrator site.

Traditional Publishing

No fee is paid to ProQuest; your dissertation will be available in full text to subscribing institutions only through the ProQuest Dissertations & Theses Global ; If you have an embargo, your dissertation will be unavailable for viewing or purchase through the subscription database during the embargo period.

Open Access Publishing Plus

$95 fee to ProQuest; your dissertation will be available in full text through the Internet to anyone via the ProQuest Database ; if you have an embargo, your dissertation will be unavailable for viewing through the open access database during the embargo period.

Optional Service: Copyright Registration

$75 fee to ProQuest; ProQuest offers the optional service of registering your copyright on your behalf. The dissertation author owns the copyright to their dissertation regardless of copyright registration. Registering your copyright makes a public record of your copyright claim and may entitle you to additional compensation should your copyright be infringed upon. For a full discussion of your dissertation and copyright, see ProQuest’s Copyright and Your Dissertation .

If you have questions regarding the ProQuest publishing options, contact their Author and School Relations team at 1-800-521-0600 ext. 77020 or via email at [email protected] .

Princeton’s Institutional Repository, DataSpace

Each Princeton University dissertation is deposited in Princeton’s Institutional Repository, DataSpace . Dissertations will be freely available on the Internet except during an embargo period. If your dissertation is embargoed, the PDF will be completely restricted during the embargo period. The bound copy, however, will be available for viewing in the Mudd Manuscript Library reading room during the embargo.

According to the Graduate School’s embargo policy , students can request up to a two-year embargo on their dissertation, with the potential for renewal by petition. If approved, the embargo would apply to the dissertation in ProQuest, as well as in Princeton’s digital repository, DataSpace . Students in the sciences and engineering seeking patents or pursuing journal articles may be approved for a shorter embargo period. Students must apply for the embargo during the Advanced Degree Application process . More information can be found on the Graduate School's Ph.D. Publication, Access and Embargoing webpage .

Those who have been approved for the embargo can choose "Traditional Publishing" or "Open Access Plus" publishing when they complete their online submission to ProQuest. Mudd Manuscript Library staff will apply the embargo in the ProQuest ETD system at the time of submission of materials to the Library. In the case of Open Access Plus, the dissertation would become freely available on the ProQuest open access site when the embargo expires. The embargo in ProQuest will also apply to the embargo in Princeton’s digital repository, DataSpace

Those who wish to request a renewal of an existing embargo must email Assistant Dean Geoffrey Hill and provide the reason for the extension. An embargo renewal must be requested in writing at least one month before the original embargo has expired, but may not be requested more than three months prior to the embargo expiration date. Embargoes cannot be reinstituted after having expired. Embargoes are set to expire two years from the date on which the Ph.D. was awarded (degrees are awarded five times per year at Board of Trustee meetings); this date will coincide with the degree date (month and year) on the title page of your dissertation. Please note: You, the student, are responsible for keeping track of the embargo period--notifications will not be sent.

To find the exact date of an embargo expiration, individuals can find their dissertation in DataSpace , and view the box at the bottom of the record, which will indicate the embargo expiration date.
The Graduate School will inform the Mudd Library of all renewals and Mudd Library staff will institute the extensions in ProQuest and DataSpace .
Princeton University Archives' Dissertation Formatting Requirements (PDF download) document provides detailed information on how to prepare the dissertation PDF and bound volume (if you are required to submit a bound volume). Please take special note of how to format the title page (a title page example is downloadable from the upper-right-hand side of this webpage). The title page must list your adviser’s name.
ProQuest's Preparing Your Manuscript guide offers additional information on formatting the PDF. Where there are discrepancies with the Princeton University Archives Dissertation Formatting Requirements document, the Princeton University Archives requirements should be followed. Special consideration should be paid to embedding fonts in the PDF.
ProQuest ETD Administrator Resources and Guidelines web page offers several guides to assist you in preparing your PDF, choosing publishing options, learning about copyright considerations, and more.
ProQuest's Support and Training Department can assist with issues related to creating and uploading PDFs and any questions regarding technical issues with the online submission site.

Whether a student pays fees to ProQuest in the ETD Administrator Site depends on the publishing option they choose, and if they opt to register their copyright (if a student selects Traditional Publishing, and does not register their copyright, no charges are incurred). Fees are to be submitted via the UMI ETD Administrator Site. Publishing and copyright registration fees are payable by Visa, MasterCard, or American Express and a small service tax may be added to the total. The options listed below will be fully explained in the ETD Administrator site.

Traditional without copyright registration: $0 to ProQuest (online)
Traditional with copyright registration: $75 to ProQuest (online)
Open Access without copyright registration: $95 to ProQuest (online)
Open Access ($95) with copyright registration ($55): $150 to ProQuest (online)

Degrees are granted five times per year at Board of Trustee meetings. Deadlines for materials to be submitted to the Mudd Manuscript Library are set by the Office of the Graduate School . The title page of your dissertation must state the month and year of the board meeting at which you will be granted your degree, for example “April 2023.”

Academic Year 2024-2025

Friday, August 30, 2024, degree date "September 2024"
Thursday, October 31, 2024, degree date "November 2024"
Tuesday, December 31, 2024, degree date "January 2025"
Friday, February 28, 2025, degree date "March 2025"
Thursday, May 8, 2025, degree date "May 2025"

Please note: If a student is granted an extension for submission of their materials after a deadline has passed, the Mudd Manuscript Library must have written confirmation of the extension from the Office of the Graduate School in the form of an email to [email protected] .

One non-circulating , bound copy of each dissertation produced until and including the January 2022 degree list is held in the collection of the University Archives. For dissertations submitted prior to September 2011, a circulating , bound copy of each dissertation may also be available. Information about these dissertations can be found in Princeton University Library's catalog .

Electronic Copy (PDF) in ProQuest

ProQuest Dissertation Publishing distributes Princeton University dissertations. Members of the Princeton University community can access most dissertations through the ProQuest Dissertations and Theses subscription database, which is made available through the Princeton University Library. For students that choose "Open Access Plus publishing," their dissertations are available freely on the internet via ProQuest Dissertations and Theses . Dissertations are available for purchase through ProQuest Dissertation Express . Once the dissertation has been accepted by the Mudd Library it will be released to ProQuest following the Board of Trustee meeting on which your degree is conferred. Bound copies ordered from ProQuest will be printed following release. Please note, dissertations under embargo are not available in full text through the ProQuest Dissertations and Theses subscription database or for sale via ProQuest Dissertation Express during the embargo period.

Electronic Copy (PDF) in Princeton's Institutional Repository, DataSpace

Beginning in the fall of 2011, dissertations will be available through the internet in full-text via Princeton's digital repository, DataSpace . (Embargoed dissertations become available to the world once the embargo expires.)

Interlibrary Loan

Dissertations that have bound copies and are not under embargo are available through Interlibrary Loan (ILL) to libraries in the United States and Canada, either through hard copy or PDF. If PDFs are available, they can be sent internationally.

Master's Thesis Submission Process

Students who are enrolled in a thesis-based Master’s degree program must upload a PDF of their thesis to Princeton's ETD Administrator site (ProQuest) just prior to completing the final paperwork for the Graduate School. These programs currently include:

The Department of Chemical and Biological Engineering (M.S.E.)
The Department of Civil and Environmental Engineering (M.S.E.)
The Department of Computer Science (M.S.E.)
The Department of Electrical and Computer Engineering (M.S.E.)
The Department of Mechanical and Aerospace Engineering (M.S.E.)
The Department of Operations Research and Financial Engineering (M.S.E.)
The Department of Near Eastern Studies (M.A.)

The PDF should be formatted according to our Dissertation Formatting Requirements (PDF download). The Mudd Library will review and approve the submission upon notification from the Graduate School that your final paperwork is ready for this step. Bound copies are no longer required or accepted for Master's theses.

Students who are not in a thesis-based Master's degree program do not need to make a submission to the library upon graduation. If you have questions, please complete the form on the Ask Special Collections page.

Dissertation Formatting Requirements

Building Science Thesis

Semester(s): spring
Discipline(s): buildingScience
Douglas E. Noble , Ph.D., FAIA, FTI Fellow

This course has several coincident agendas. We will complete the Master’s Thesis for the Building Science program which each student has developed in preceding 596 and 692a classes. But in the process, we will address a broad range of ancillary topics. We will create a “culture of learning” as part of the course. Although it is a studio course, there will be guest lecturers, lectures of assigned topics and periodic reviews, as well as normal studio time. We will review the scientific method in general and as it applies to each thesis topic. We will consider the value and impact of investigative tools in the process and product of Architecture. We will write papers which could be submitted to conferences or journals as a prototype of technology transfer (and a measure of the value and validity of the material.) Those of you who have had abstracts accepted will use the abstracts as topics for these papers. We will do several interim presentations to the first year students and to outside consultants and to committee members, prior to the final presentation. We will examine topics in Building Science which are of current interest, whether or not one of the current theses addresses these topics. We will write the thesis in several stages, so that there is opportunity to modify and improve both the research and the writing prior to the thesis due date. Prior to the due date, each student will produce a thesis in the format acceptable to the University and with content acceptable to all committee members. Finally, each student will produce a shorter version of the thesis material in a format consistent with publication. In the process, each student will learn something about the content area of each other student’s thesis.

Prerequisite(s): ARCH 596

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507 Theories of Computer Technology Theories of Computer Technology Building information modeling (BIM) is one of the hottest topics in the architecture / engineering / construction profession (AEC) today. Learn what it is (3d parametric modeling), common software tools (this class concentrates primarily on Revit Architecture and some Navisworks), how it relates to sustainable design issues (Vasari and Green Building Studio), and why it is useful to the AEC industry (including being able to create awesome adaptive components!). Although offered in the School of Architecture, the techniques taught are equally applicable to others with an interest in the applications of BIM. Building science majors, structural engineering students, construction management students, and others are strongly encouraged to enroll. It is assumed that students already have a basic understanding of 2D CAD and 3D digital modeling. GO TO COURSE PAGE ↗

Studies of construction system development within the architectural design context; processes and issues of selection, evaluation, optimization, integration, design control, and innovation, Departmental approval required.

511L Seminar Building Systems Seminar Building Systems Develop an understanding of building materials and assemblies and their characteristics, impacts, and performance. Topics covered include building envelope performance and aesthetics, environmental systems (heating, cooling, daylighting, and acoustics), and basic principles of construction. Students will also develop an understanding of the financial implications of building components and systems. GO TO COURSE PAGE ↗

This seminar emphasizes the study of horizontal structures, with a focus on the integration of building systems and exploring the fit and synergy of form and structure. Develop informed intuition for structures, their response to natural forces (gravity, seismic, thermal, wind), and how structure interacts with other design issues. Identify strategies and explore issues and problems in the development of building structure systems such as design criteria, system selection, design development, optimization, and system integration. Seismic design and seismic failure will also be introduced. Learn the basics of Multiframe and LDG (Lateral Design Graph) to design for lateral wind and seismic load.

Required text:

Structure and Design: https://titles.cognella.com/structure-and-design-9781516522989

Detailed information is posted at http://uscarch.com/structures/

515L Seminar Advanced Environmental Systems Seminar Advanced Environmental Systems The course is intended to give the students both a fundamental and practical knowledge of building environmental control systems and strategies in thermal, air quality, lighting, and acoustic conditions in large and small buildings. It also provides a working knowledge of many of the interrelated building systems necessary to support human physiological benefits: environmental comfort and health effects. Much of the material covered in this course will help to prepare the student in direct way for the professional building environmental design. GO TO COURSE PAGE ↗

Architects are designing increasingly complex building skins using new materials and processes that were not imaginable just a few years ago. This course is intended to provide a solid foundation of building envelope design issues and technology while exposing students to some of the most advanced building skins today.

No previous facades experience required for this course.

519 Sustainability in the Environment: Infrastructures, Urban Landscapes, and Buildings Sustainability in the Environment: Infrastructures, Urban Landscapes, and Buildings Working with established and emerging environmental management frameworks, this course aims to explore and apply practical (and measurable) approaches to address urban sustainability challenges at the street, neighborhood, district, and municipal scale with a focus on regions within the greater Los Angeles area as laboratories for investigation. The course generates an overall picture of L.A.'s metabolism to map and analyze resource flows and to examine the city’s ecological footprint. It evaluates where and how resources are used and where action might be taken to transform existing infrastructures, landscapes and buildings to meet sustainability performance goals established by the city of Los Angeles, the State of CA, and the class. GO TO COURSE PAGE ↗
523aL Structural Design and Analysis Structural Design and Analysis Introduction to behavior and analysis of building structures. Structural loading, materials, and element types will be explored to understand the basic building blocks of buildings. GO TO COURSE PAGE ↗
523bL Structural Design and Analysis Structural Design and Analysis Investigation and design of building structural systems for gravity, wind and seismic loading. Comprehensive design exploration of framing type, materials, detailing, layout, form and integration. GO TO COURSE PAGE ↗

This course will concentrate on providing students with a fundamental technical skillset applicable to the design and delivery of high-performance façade systems. The predominant focus will be the design of contemporary glazed curtainwalls and rainscreen systems in their many forms.

The building façade system uniquely combines elements of performance and architectural expression like nothing else in architecture. It is a highly complex system that requires a detailed and comprehensive exploration of myriad, often competing, variables that converge at the building skin. Increasingly, architectural practice demands expert knowledge of the complexities of the façade system to realize building performance and budget goals. The façade system plays a defining role in a building’s appearance, a pivotal role in resilience and sustainability outcomes, and is critical to the health, wellness and productivity of building occupants. In addition, it typically represents 15-25% of a project’s construction budget. Façade system skills are vital for the successful practice of architecture in producing healthy, cost-effective, resilient and sustainable buildings and urban habitat.

This course intends to provide the student this basic skillset, including the fundamental building physics and performance criteria that each façade system design must accommodate involving the performative behaviors of thermal mechanics, water vapor and air transport in various materials, moisture and condensation management, and airflow and rainwater control. The course content will familiarize the student with the basic building physics, tools and techniques required to successfully design and deliver a responsive high-performance façade system. Upon completion of this class, students will be able to develop façade system performance parameters for a given project, explore materials that meet the prescribed parameters, and develop a basic façade system design and details of construction to realize the design aspirations and technical requirements of the project.

573 Seismic Design Seismic Design Develop informed intuition for structural lateral systems strategies and layout required for seismic design. Understand the characteristics of earthquakes and the systems that resist them. Integrate seismic design into the overall architectural design of buildings including the detailing requirements for structural and nonstructural components. (From 2012 Syllabus) "Earthquakes and how they influence building design will be the subject of this course. Students will learn about the earth science behind earthquakes and the fundamentals of the physics and behavior of structural systems designed to resist earthquake motions. System and material selection for seismic design considering the structure, façade, and nonstructural components will be explored to help the student make informed decisions about seismic design." GO TO COURSE PAGE ↗
575a Systems The Thermal Environment Systems The Thermal Environment Learn to apply the fundamental scientific principles governing the thermal environment and human physiology to contemporary issues of environmentally responsive building design and resource efficiency. Students will explore the technologies and strategies to control the indoor environment as well as the basic analyses needed to inform design decision-making and examine project performance. The course will cover the laws of thermodynamics, heat transfer and solar geometry in the context of building design and operation, and occupant comfort - the building as an environmental filter, where environmentally responsive design strategies are used to minimize the size and operation of mechanical systems and demand for energy from renewable sources. Following these steps, energy efficient mechanical systems, controls, and renewable energy technologies will be covered as a supplement to these strategies. GO TO COURSE PAGE ↗
575b Systems Luminous and Auditory Phenomena in Architecture Systems Luminous and Auditory Phenomena in Architecture This course is the second in the building systems series and covers topics of lighting and acoustics. The fundamental scientific principles governing light and sound in the built environment will be examined in the context of human physiological, psychological and biological needs. It exposes students to technologies, materials and strategies for control of light and sound in buildings as well as the basic analyses needed to inform design decision-making and examine project performance. The course will continue the themes of resource efficiency and end-user comfort through the examination of emerging metrics for daylight sufficiency, visual and acoustic comfort. GO TO COURSE PAGE ↗
576 Sustainable Design for Healthy Indoor Environments Sustainable Design for Healthy Indoor Environments This course will expose seniors and/or graduate students to a systematic evaluation process for performing and diagnosing indoor environmental quality relative to thermal, lighting, air quality, acoustic, and spatial conditions in buildings. Emphasis will be on fundamental approaches for developing integrated environmental design methods that are primary requirements for students in the fields of architecture, environmental design, and building science. This knowledge is basic to understanding the principles underlying human-architecture interaction. The course will focus on the building design process required to assure indoor environmental quality (IEQ) and the needs of building occupants to promote their environmental health, work productivity, psychological comfort, aesthetic quality, and satisfaction. Technical applications will involve user surveys, environmental data collection, and in-depth analysis, as well as suggested steps and processes for solving environmental problems. Course content is designed to help students develop a framework for addressing architectural design and research problems and for identifying practical solutions to the design planning process that will assure a successful building project. GO TO COURSE PAGE ↗
577 Lighting Design Lighting Design The artist, the scientist and the architect who want to understand their world are fascinated by light. Light is the medium of perception in art and in architecture. Light is also one of the most fascinating aspects of physics. As far as we know, it is the only constant. Indeed time and space warps around the constant speed of light. Examine the perceptual and physical aspects of light and learn how the design profession has used light, the tools with which it studies light, and the design principles and drawing conventions with which the profession manipulates light in buildings. GO TO COURSE PAGE ↗
588 Interactive Architecture Computing and the Physical World Interactive Architecture Computing and the Physical World This course is a seminar and workshop exploring physical interaction with computational media in real time. The widespread diffusion of sensing, computational, and communicative media into the physical realm presents an opportunity for exploring and constructing intelligent objects understood through dynamic and complex relationships of adaptation and improvisation to the environment, the site, and the human body. The course will chart and explore a range of approaches for integrating computation into the physical realm through a series of projects using physical computing prototyping tools. This course is focused on self-directed, project-based learning within and experimental and collaborative setting. Students will design and develop projects that use sensors and microcontrollers to translate sensory input to control electro-mechanical devices such as motors, servos, lighting or other hardware in real time. There are no prerequisites for the class. This is an interdisciplinary course and students from outside the School of Architecture are welcomed and encouraged to register GO TO COURSE PAGE ↗

Vertical structures respond to gravity, wind, seismic, and thermal loads. They also need to be integrated with architectural objectives, creating a synergy of form and structure. This course covers various methods for stabilizing vertical structures, including foundation design, moment and braced frames, framed tube design, shear walls, and building diaphragm design in the context of wood, steel, concrete, and masonry structures. Students will explore the use of Multiframe; LDG (Lateral Design Graph); SDG (Structure Design Graph) to design moment frames, braced frames, and shear wall structures; and PDG (Post Design Graph) to design posts in wood, steel, concrete, and masonry for axial and bending stress.

Required text

615L Seminar Environmental Systems Research Seminar Environmental Systems Research Acquire new building science concepts, and experience how they impact building performance. This course introduces the concept of total building energy performance, delineating the full range of performance mandates required for today’s architecture, including building integrity. Explore the relationships, opportunities, and conflicts of the performance mandates, and the integration of building systems necessary to achieve total building energy performance. Through lectures and seminar instruction, students will develop a basis for environmental design performance and system design skills, towards creating high-performance buildings. GO TO COURSE PAGE ↗

The course will focus on the development of novel façade system solutions—solutions responsive to the shortcomings of contemporary façade systems—with an emphasis on their application in both new and existing buildings.

694 Research Publication Methods for Building Science Research Publication Methods for Building Science Technical documentation, graphic representation, and verbal presentation for writing and presenting journal articles and conference presentations in building science. GO TO COURSE PAGE ↗

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Integrated Human Services (B.S.)
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Master of Science in Counseling, Clinical Mental Health Concentration

On this page:

Why Choose Clinical Mental Health Counseling?

Real-world experience, success stories, academics and curriculum, frequently asked questions, program outcomes.

This degree qualifies you to earn professional counseling licensure (LPC) in Ohio ( https://cswmft.ohio.gov/ ) by taking the National Counseling Exam (NCE). Licensure and certification requirements vary from state to state, and we have not determined if this program meets educational requirements outside of Ohio. If you are planning to pursue professional licensure or certification in a state other than Ohio, please contact the appropriate licensing entity in that state to seek information and guidance regarding that state’s licensure or certification requirements.

CMHC Narrative Report

In 2022-23, there were 30 graduates of Wright State University's Clinical Mental Health Counseling program. Our graduates consistently achieve high pass rates on licensing examinations. The pass rate for graduates taking the NCE examination in the 2022-23 school year was 86 percent on their first attempt and 100 percent for those who took the test a second time. Of the 30 students who graduated, 72 percent graduated from the program in the expected time period (within 3 years), and 100 percent of those program graduates responding to surveys were employed in the field within six months of graduation.

Year: 2022-23
Current Students: 191
Number of Graduates: 30
Completion Rate: 72 percent*
NCE Exam Pass Rate: 93 percent
Placement: 100 percent**

*Represents the percentage of students that graduated within the 3-year expected time frame. Many students who exceed this time frame did so due to the pandemic and/or balancing full-time jobs, family responsibilities. Our program does not require students to attend full-time and many elect to be enrolled part time which will require more semesters to graduate. **Represents the percentage of students who were employed in the field within 6 months of their graduation.

Contact Information

Josh Francis, Ph.D., LPCC-S, LICDC-CS Associate Professor Director, Clinical Mental Health Counseling Program College of Health, Education, and Human Services 387 Millett Hall 937-775-2150 [email protected]

Admission Deadlines

The graduate counseling programs review and admit new students twice a year. You are eligible to begin your program the following semester after you are admitted (i.e., if you are admitted in the fall, you are eligible to begin taking classes during the spring semester, and so on.) The deadline to have all required application materials submitted to The Graduate School is as follows:

Spring admission (January start date) application is due August 1.
Fall admission (August start date) application is due December 1.
Only complete applications will be forward to the Department of Human Services office for review. Please check with the Graduate School at 937-775-2976 to determine if your application is complete. After department review, successful candidates will be invited to a required group interview (successful candidates will receive an invitation via mail). Group interview details are provided in the invitation letter.

Admission Procedures

In addition to the University and College of Graduate Programs requirements, the Department entrance eligibility requirements include the following:

A minimum 3.0 GPA is required to enter the program. Applicants can be admitted conditionally if cumulative GPA is below 3.0 with department approval.
A minimum of three letters of recommendation.
A statement of educational and professional goals.
Candidates are invited to a group interview only after applications are completed by the deadline date (required GPA, 3 letters) and their application is reviewed and approved by the Department.
Poise and affect in the interview situation
Apparent commitment to field
Knowledge of professional role
Appropriate motivation towards role
A realistic personal appraisal of the strengths and weaknesses the candidate would bring to the field
Group participation and interaction
Interpersonal skills
Knowledge of technological competence and computer literacy
Cultural sensitivity and understanding of diversity
Candidates accepted into the program will be notified by the College of Graduate Programs approximately two weeks after the group interview. The accepted candidates will be invited to an orientation session that will occur approximately two-three weeks after the notification of acceptance. The orientation is an opportunity to get answers to questions candidates have about their future education in HS. Note : Individuals with special needs should notify the department in advance to arrange for assistance.

Apply to Graduate School.

Questions about the graduate school application? Contact:

The College of Graduate Programs and Honors Studies Location: 160 University Hall Hours: Monday-Friday 8:30 a.m.-5 p.m. Phone: 937-775-2976 Fax: 937-775-2453 Email: [email protected]

View the content we recently presented at our Graduate Counseling Program Virtual Open House (PDF) .

The Addictions counseling program and the Clinical Rehabilitation Counseling (CRC) program are offered 100% online but there are face-to-face courses available as an option. The School Counseling and Clinical Mental Health Counseling (CMHC) programs offer online classes, but they are not fully online. Currently a little more than half of our courses can be completed online. There are classes that are only offered in person due to the nature of the content.

You can find information on cost and financial aid at Graduate and Professional Tuition and Fees .

A limited number of graduate assistantships are offered within the Department of Human Services (DHS) and throughout the university. We encourage students to apply for any graduate assistantship positions that are available. Please note that not all GA positions are listed in Handshake as the link mentions, but the application process is the same for all available positions.

There is a graduate tuition scholarship program offered through the Graduate School.

This largely depends on how many courses you decide to complete each semester. The School Counseling and CMHC are both 60 credit hour programs. The Addictions Counseling Program is a 63-credit hour program and the CRC program is 69 credits. For full time students, the programs take 2.5- 3.5 years to complete. Graduate students are encouraged to work closely with their faculty advisor to prevent delays in completion.

The number of classes depends on the individual student and the load they are capable of taking each semester. Six credit hours is considered full time for graduate students at Wright State University. This is typically two courses in our programs. Students take more or less depending on their situation. Students are able to take different course loads each semester but should be mindful of how full-time vs part time status can affect scholarships and financial aid. The faculty advisor is your main resource for ensuring you are on track for program completion.

Yes. All graduate classes are scheduled outside of business hours and we have many wonderful students who are full-time teachers and other full-time employees. However, students who work full time as a classroom teacher have a very difficult time completing the 100 hour practicum and 600 hour internship experience in the school setting. Unless time will be allotted by the building administrator to complete practicum and internship hours during the contracted school day, this will be a challenge. This is something to consider before beginning the program. Full time teachers interested in applying for the program are encouraged to reach out to Dr. Neyland-Brown, school counseling program director, to discuss if this is feasible.

All graduate classes are scheduled outside of business hours and we have many wonderful students who are full time teachers and other full time employees. Classes are scheduled in the evenings beginning as early as 4:40 p.m. and as late as 7:20 p.m. Each class meets once/week for 2 hours and 40 minutes. Online classes may be synchronous or asynchronous.

You can find this information under admission deadlines .

The GRE has temporarily been waived however, students who do not meet the minimum GPA requirement and who opt out of submitting a GRE score will need to submit a Graduate Admission Petition to the graduate school prior to submitting the application.

Contact the program director of each program for more information:

Addictions Counseling: Dr. Mary Huber
Clinical Mental Health: Dr. Huma Bashir or Dr. Joshua Francis
Clinical Rehabilitation Counseling: Dr. Mary Huber
School Counseling: Dr. Neyland-Brown

Dr. Neyland-Brown is dually licensed in both School Counseling and CMHC. She can help you explore the possibility of pursuing both licenses and whether this is a good fit for you.

View our graduation requirements .

Graduate students in the CMHC, CRC, School Counseling and Addictions Counseling program are not required to complete a thesis. As part of program completion and to be eligible for graduation, Students are required to take and pass the appropriate licensure exam. If a student does not pass or does not take the exam before they graduate they will need to complete the department comprehensive exam.

All four programs are CACREP accredited programs. Find more information on accreditation .

Addictions counseling graduates will apply for the LPC (Licensed Professional Counselor) license and work on the requirements for a Chemical Dependency (CD) license .
Clinical Rehabilitation Program graduates will apply for the CRC (Clinical Rehabilitation Counselor) by the and have the option of applying for the LPC (Licensed Professional Counselor) license.
Clinical Mental Health Counseling graduates will apply for the LPC (Licensed Professional Counselor) license .
School Counseling graduates will apply for the Pupil Services License issued by the Ohio Department of Education.

Anyone with a master’s degree and/or is pursing masters in any programs in the DHS and other related fields are eligible to apply for Trauma-Informed Counseling Certificate , e.g., licensed social worker, clinical psychologist school administrators, school personnel, and nursing.

Please contact Dr. Huma Bashir or Dr. John Conteh for more information.

Per our accreditation standards, we currently only allow transfer coursework from other CACREP accredited counseling programs. Any transfer courses are at the discretion of the program. Please contact a program director for an appropriate review.

Quite a few of our students commute from Columbus, Cincinnati, Lima, and parts of Indiana. Although we offer many online courses, students are required to come on campus for courses delivered in person.

All students are required to complete the department comprehensive exam or the appropriate exam for licensure in the state of Ohio as a graduation requirement. Most students opt to take the licensure exam. If you plan to practice in another state, you will want to explore licensure requirements for that state. Another state examination cannot be substituted for program completion.

Clinical Mental Health Counseling

You will learn theories and techniques for counseling individuals, families and groups, and administration and interpretation of psychometric assessment. This major has all the courses required to qualify for admission to the Ohio Professional Counselor licensure exam (NCE).

Last year, there were 33 graduates of Wright State’s clinical mental health counseling program. Our graduates consistently achieve high pass rates on licensing examinations. The pass rate for graduates taking the NCE examination in 2018-2019 was 80 percent. Of the admitted students, 80 percent graduate from the program during the expected time period.

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Fall 2021 Robotics Master’s Thesis Presentations

December 17, 2021 @ 11:30 am - 12:30 pm.

This event was broadcast via Zoo m

1 1:30AM – Welcome Remarks Dr. Ani Hsieh – ROBO Program Graduate Chair

11:35AM – Shiyani Patel Advised by Dr. Kostas Daniilidis “Curiosity based object pose estimation”

11:55AM – Yuchen Sun Advised by Dr. Cynthia Sung “Valid Jumping Locomotion of an Origami Jumping Robot REBOund”

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Cyber Security and Privacy (MS) – Accelerated BS to MS Track

Program at a glance.

In State Tuition
Out of State Tuition

Learn more about the cost to attend UCF.

U.S. News & World Report Best Colleges - Most Innovative 2024

The Accelerated BS to MS program in Cyber Security and Privacy (Technical Track only) allows highly qualified UCF undergraduate majors in Computer Science (CS) or Information Technology (IT) to take graduate-level courses that will count toward their MS degree while completing their BS degree program. Students must apply to the MS program.

Up to 9 credit hours of 5000- and 6000-level courses with a grade of "B" (3.0) or better may be counted toward the accelerated BS to MS program. Two additional requirements for the students in this program are:

Students must earn at least a "B" (3.0) in each undergraduate- or graduate-level course counted for the program.
Students must opt for this program no later than the beginning of their junior year.

Total Credit Hours Required: 30 Credit Hours Minimum beyond the Bachelor's Degree

University of Central Florida Colleges

Track Prerequisites

This track is available to University of Central Florida undergraduate majors in Computer Science (CS) or Information Technology (IT) only.

Degree Requirements

Required courses.

CAP5150 - Foundations of Computer Security and Privacy (3)
CDA5220 - Foundations of Secure Execution Environment (3)
CIS6614 - Advanced Software Systems Security (3)

Elective Courses

At least half of the credit hours used to meet program requirements must be at the 6000 level.
CAP5151 - Internet of Things Security and Privacy (3)
CIS5730 - Blockchains and Smart Distributed Contracts (3)
CDA6221 - Advanced Topics in Secure Execution Environment (3)
CNT5410L - Cyber Operations Lab (3)
CAP6135 - Malware and Software Vulnerability Analysis (3)
CIS6395 - Incident Response Technologies (3)
CNT5008 - Computer Communication Networks Architecture (3)
CNT6707 - Advanced Computer Networks (3)
CNT5805 - Network Science (3)
COP5711 - Parallel and Distributed Database Systems (3)
COP6731 - Advanced Database Systems (3)
CAP5610 - Machine Learning (3)
CAP5636 - Advanced Artificial Intelligence (3)
CAP6640 - Computer Understanding of Natural Language (3)
Earn at least 6 credits from the following types of courses: Electives from the groups listed above, and/or the following electives: COP 5611 – Operating System Design Principles CDA 5106 – Advanced Computer Architecture COT 5405 – Design and Analysis of Algorithms COT 6410 – Computational Complexity CEN 5016 – Software Engineering

Thesis/Nonthesis

Earn at least 6 credits from the following types of courses: XXX 6971 Thesis (prefix determined by disciplinary area of your thesis adviser, e.g., CAP, CDA, CEN,COP or COT 6971) Six credits of thesis are required with the professor who directs the student's thesis. The thesis experience is expected to span two semesters. Thesis students who are full-time must continue to enroll in 3 credit hours of thesis course work until the thesis requirement is satisfied, even if it goes beyond the minimum of 6 credit hours of thesis. Students are required to prepare and defend a formal thesis in accordance with university requirements.
Earn at least 6 credits from the following types of courses: The nonthesis option requires completing 6 credit hours of any electives in both Technical Track and Interdisciplinary Track beyond the 15 credit hours of electives described above.

Grand Total Credits: 30

Financial information.

Graduate students may receive financial assistance through fellowships, assistantships, tuition support, or loans. For more information, see the College of Graduate Studies Funding website, which describes the types of financial assistance available at UCF and provides general guidance in planning your graduate finances. The Financial Information section of the Graduate Catalog is another key resource.

Fellowship Information

Fellowships are awarded based on academic merit to highly qualified students. They are paid to students through the Office of Student Financial Assistance, based on instructions provided by the College of Graduate Studies. Fellowships are given to support a student's graduate study and do not have a work obligation. For more information, see UCF Graduate Fellowships, which includes descriptions of university fellowships and what you should do to be considered for a fellowship.

In this accelerated BS to MS track, students are required to pursue the Technical Track of the Cyber Security and Privacy MS program.

A group of students working on a coding project

MS in Computer Science — Online

Elevate your career with our fully online ms in computer science..

Our 30-credit program offers the same academic rigor and high quality that you would find on campus.

Complete advanced coursework taught by award-winning faculty with broad industry expertise. Deepen your technical knowledge base. Cultivate specialized skills in areas like AI, machine learning, and cybersecurity. You'll come away with proficiency in:

Software design, implementation, and testing
Solving computing problems by applying algorithmic and theoretical computer science principles

Plan on two to four years to complete your graduate degree, depending on your course load each semester. Degree requirements include:

30 credits meeting the course requirements
Four core courses, with at least one theory, systems, or artificial intelligence class, such as Algorithms for Data Science and Intro to Computer Network Security
12 credits in 600-level classes

Featured classes

In this course, students will learn the fundamentals behind large-scale systems in the context of data science. We will cover the issues involved in scaling up and out parallelism in order to perform fast analyses on large datasets.

Topics covered may include advanced file carving and reconstruction, forensic analysis of modern file systems, network forensics, mobile device forensics, memory forensics, and anti-forensics.

This course provides an in-depth examination of the principles of distributed systems and advanced concepts in operating systems. Covered topics include client-server programming, distributed scheduling, virtualization, and cloud computing.

Benefits list

Abstract image of cars driving on a digital road

Career Outlook

Our graduates are sought after by leading graduate schools, cutting-edge start-ups, and industry giants across sectors — from health care and finance to technology and the arts. Nine out of ten of our MS graduates are employed or continuing their education.

Rigorous and Practical

Offering the same academic rigor as our in-person program, the online MS in computer science will give you the practical skills needed to take on today’s biggest societal challenges across many disciplines — from technology and finance to health care and beyond.

Application information & deadlines

This program is intended for students with a solid background in computer science and mathematics. An undergraduate degree in computer science is ideal but by no means required.

Spring Admission

October 1, 2024.

Priority deadline; Nov. 15: final deadline

Fall Admissions

June 1, 2025.

Priority deadline; July 15: final deadline

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IMAGES

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MS in Computer Science
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