Mo Chen directs the Multi-Agent Robotic Systems Lab at Simon Fraser University. The lab’s research focuses on principled robotic decision making, centred around combining traditional analytical methods in robotics and modern data-driven techniques. The lab addresses theoretical and computational challenges in robotic safety and human-robot interactions. This is achieved by connecting control theory, computer vision, and reinforcement learning. Through incorporating human knowledge and understanding of robotic systems into data-driven algorithms, Chen aspires to make robots safer and smarter to enable more widespread use of robotic systems such as autonomous cars, unmanned aerial vehicles, and household robots. Chen’s interdisciplinary research spans the fields of control theory, robotic safety verification, computer vision, reinforcement learning, and human-robot interactions.
- Asayesh, S., Chen, M., Mehrandezh, M., & Gupta, K. (2020). Least-Restrictive Multi-Agent Collision Avoidance via Deep Meta Reinforcement Learning and Optimal Control. IEEE.
- Li, A., Bansal, S., Giovanis, G., Tolani, V., Tomlin, C., & Chen, M. (2020, July). Generating robust supervision for learning-based visual navigation using hamilton-jacobi reachability. In Learning for Dynamics and Control (pp. 500-510). PMLR.
- Bansal, S., Chen, M., Tanabe, K., & Tomlin, C. J. (2020). Provably Safe and Scalable Multivehicle Trajectory Planning. IEEE Transactions on Control Systems Technology.
- Chen, M., Herbert, S. L., Hu, H., Pu, Y., Fisac, J. F., Bansal, S., ... & Tomlin, C. J. (2020). FaSTrack: a Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking. Transactions on Automatic Control (TAC).
- Ivanovic, B., Harrison, J., Sharma, A., Chen, M., & Pavone, M. (2019, May). Barc: Backward reachability curriculum for robotic reinforcement learning. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 15-21). IEEE.
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