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I direct the Trustworthy Robotics Lab, where we design algorithms that can enable general-purpose robots and autonomous systems to operate capably, safely, and securely with humans, while remaining resilient to real-world failures and uncertainty. To achieve this, we leverage control and machine learning, while connecting to optimization, perception, formal methods, planning, human-robot interaction, and statistics. I believe strongly in validating that the theoretical guarantees of my algorithms translate to the real world when deployed on hardware. I'm interested in a broad range of applications, including robotic manipulation, vision-based navigation, aerospace autonomy, and the control of large-scale cyber-physical systems.
My teaching interests span robotics and robot learning, trustworthy autonomy, control theory, and computer security. In control, I am interested in teaching about feedback systems at both the undergraduate and graduate levels, covering linear, nonlinear, and optimal control with an emphasis on rigorous analysis and real-world implementation. I aim to connect state-space methods and computational tools to modern applications in robotics. I am also interested in teaching about scalable methods for formal verification with applications in robotics and safe machine learning for autonomy, equipping students to design provably reliable cyber-physical systems. I also teach computer security with at the undergraduate level.
- Nath*, Yin*, and Chou. Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees. 8th Annual Learning for Dynamics & Control Conference (L4DC), oral presentation, June 2026.
- Zhan, Chiu, Leeman, and Chou. Robustly Constrained Dynamic Games for Uncertain Nonlinear Dynamics. IEEE International Conference on Robotics and Automation (ICRA), June 2026.
- Li and Chou. A Convex Formulation of Compliant Contact between Filaments and Rigid Bodies. IEEE International Conference on Robotics and Automation (ICRA), June 2026.
- Chiu, Zhang, and Chou. Learning Constraints from Stochastic Partially-Observed Closed-Loop Demonstrations. IEEE Control Systems Letters, Jan 2026.
- Suh, Chou, Dai, Yang, Gupta, and Tedrake. Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching. 7th Conference on Robot Learning (CoRL), Nov 2023.
- Pan, Chou, and Berenson. Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification. IEEE International Conference on Robotics and Automation (ICRA), May 2023.
- Chou, Ozay, and Berenson. Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory. Algorithmic Foundations of Robotics XV, June 2022.
- Chou, Berenson, and Ozay. Learning constraints from demonstrations with grid and parametric representations. International Journal of Robotics Research (IJRR), August 2021.
- Knuth, Chou, Ozay, and Berenson. Planning With Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants. IEEE Robotics and Automation Letters (RA-L), July 2021.
- Chou, Ozay, and Berenson. Explaining Multi-stage Tasks by Learning Temporal Logic Formulas from Suboptimal Demonstrations. Robotics: Science and Systems XVI, July 2020.