Published in , 1900
Recommended citation: Hatton, R.L., Dear, T. and Choset, H., 2017. Kinematic cartography and the efficiency of viscous swimming. IEEE Transactions on Robotics, 33(3), pp.523-535. https://ieeexplore.ieee.org/abstract/document/7859387
Published in , 1900
Recommended citation: Grover, J., Zimmer, J., Dear, T., Travers, M., Choset, H. and Kelly, S.D., 2018, June. Geometric motion planning for a three-link swimmer in a three-dimensional low Reynolds-number regime. In 2018 annual american control conference (ACC) (pp. 6067-6074). IEEE. https://ieeexplore.ieee.org/abstract/document/8431828
Published in , 1900
Recommended citation: Shi, J., Dear, T. and Kelly, S.D., 2020. Deep reinforcement learning for snake robot locomotion. IFAC-PapersOnLine, 53(2), pp.9688-9695. https://arxiv.org/abs/2603.08546
Published in , 1900
Recommended citation: Dear, T., Buchanan, B., Abrajan-Guerrero, R., Kelly, S.D., Travers, M. and Choset, H., 2020. Locomotion of a multi-link non-holonomic snake robot with passive joints. The International Journal of Robotics Research, 39(5), pp.598-616. https://asmedigitalcollection.asme.org/DSCC/proceedings-abstract/DSCC2017/58288/230317
Published in , 1900
Recommended citation: Pujari, A., Lin, H., Neal, W.L., Dear, T. and Kelly, S.D., 2023. Detecting and Exploiting Symmetry to Accelerate Reinforcement Learning. In 2023 Proceedings of the Conference on Control and its Applications (CT) (pp. 103-110). Society for Industrial and Applied Mathematics. https://epubs.siam.org/doi/abs/10.1137/1.9781611977745.14
Published in , 1900
Recommended citation: Guo, G., Goldfeder, J., Ray, A., Dear, T. and Lipson, H., 2025. Deepcollide: Scalable data-driven high dof configuration space modeling using implicit neural representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2689-2699). https://openaccess.thecvf.com/content/ICCV2025W/E2E3D/html/Guo_DeepCollide_Scalable_Data-Driven_High_DoF_Configuration_Space_Modeling_using_Implicit_ICCVW_2025_paper.html
Published in , 1900
Recommended citation: Wang, Y., Syed, R., Wu, F., Zhang, M., Onol, A., Barreiros, J., Nayyeri, H., Dear, T., Zhang, H. and Li, Y., 2026. Interactive world simulator for robot policy training and evaluation. arXiv preprint arXiv:2603.08546. https://arxiv.org/abs/2603.08546
Fall 2026, Spring 2024, Fall 2021, Spring 2021, Spring 2019
This course is an introduction to fundamental problems and algorithms in robotics from a computer scientist’s perspective. While robotics is inherently a broad and interdisciplinary field, we will primarily focus on ideas with roots in computer science, as well as the roles that a computer scientist would play in robotics research or engineering tasks. Topics include configuration spaces, kinematics, search and sampling-based planning, state estimation, localization and mapping, perception, and learning.
Summer 2026, Summer 2025, Spring 2025, Summer 2024, Spring 2024, Spring 2023, Spring 2022, Summer 2021, Fall 2020, Fall 2019, Fall 2018
Artificial intelligence (AI) is a broad, interdisciplinary, and rapidly evolving field of computer science concerned with the design of systems that can perceive, reason, learn, and act. This course provides an overview of the fundamental methods and applications of AI, with an emphasis on algorithms, computation, and the evolution of the field from classical approaches to modern AI systems. Topics include search and planning, probabilistic reasoning, machine learning, reinforcement learning, deep learning, foundation models, and agentic AI. Students will develop intuition and algorithmic thinking through written and programming problems in Python, and will apply these concepts to building and evaluating intelligent systems. By the end of the course, students will be able to formulate AI problems, reason and make decisions under uncertainty, learn from data, and design modern AI systems that use foundation models and other AI techniques.
Spring 2026
COMS 4771 is a graduate-level introduction to the statistical principles and algorithmic paradigms of machine learning (ML). Broadly speaking, ML is concerned with the tasks of learning models from data, generalizing to unseen scenarios, and solving problems without explicit instructions. We will focus mostly on supervised learning, including both classical and deep learning methods. We will also see how ML is used in various applications in NLP, vision, robotics, etc. throughout the course.
Spring 2026, Fall 2025, Spring 2025, Fall 2024, Fall 2023, Fall 2022, Spring 2019
The study of discrete mathematics provides an important foundation for basic theoretical principles in computer science. We first build a strong foundation in logic, formal proofs, and mathematical induction. We then examine the discrete structures of functions, relations, and graphs, which provide different ways of representing relationships, connections, and networks. Finally, we move onto numerical and computational aspects: number theory and modular arithmetic, counting and combinatorics, and discrete probability. Throughout the course, we will also see computer science applications and practice writing implementations in Python.
Spring 2023, Spring 2022, Fall 2021, Spring 2021, Spring 2020
This course is an introduction to linear algebra and its usage in computational applications. The study of linear equations, linear functions, and their representations pervades numerous fields of study. Students will learn and practice fundamental ideas of linear algebra and simultaneously be exposed to and work with real-world applications of these ideas. This course emphasizes a rigorous approach to mathematics, which will serve as a foundation for future courses like computer graphics, machine learning, and robotics. The learning and usage of Python and libraries such as NumPy is an essential component of the course, as is the development of basic skills of computational programming.
Spring 2013 - Spring 2018 (Carnegie Mellon)
Although I was never a formal instructor for this course, I initially was a graduate student TA and then worked closely with my advisor in subsequent semesters to implement an online curriculum for present offerings of the course.
Summer 2012 (UC Berkeley)
This was my first foray into teaching as a 5-time TA and instructor for a course in introductory circuits during my undergraduate years at Berkeley.