Our research spans across robotic problems from estimation through planning to control with a focus on efficent distributed algorithms for multi-robot platforms.

Learning Dynamical Systems

  1. R. N. Haksar and M. Schwager, “Learning Large Graph-based MDPs with Historical Data,” IEEE Transactions on Control of Network Systems, 2021. [pdf] [bibtex]
  2. X. Lan and M. Schwager, “Learning a dynamical system model for a spatiotemporal field using a mobile sensing robot,” in 2017 American Control Conference (ACC), May 2017, pp. 170–175. [pdf] [bibtex]

Network Analysis

  1. J. A. Vincent and M. Schwager, “Reachable Polyhedral Marching (RPM): A Safety Verification Algorithm for Robotic Systems with Deep Neural Network Components,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021. [pdf] [bibtex]

Learning Controllers

  1. R. N. Haksar and M. Schwager, “Distributed Deep Reinforcement Learning for Fighting Forest Fires with a Network of Aerial Robots,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2018, pp. 1067–1074. [pdf] [bibtex]
  2. D. Aksaray, A. Jones, Z. Kong, M. Schwager, and C. Belta, “Q-Learning for robust satisfaction of signal temporal logic specifications,” in 2016 IEEE 55th Conference on Decision and Control (CDC), Dec. 2016, pp. 6565–6570. [pdf] [bibtex]

Learning Optimization Solutions