Jonathan How

Jonathan P. How is the Richard C. Maclaurin Professor of Aeronautics and Astronautics in MIT’s Department of Aeronautics and Astronautics. His research interests include navigation and control; design and implementation of distributed robust planning algorithms to coordinate multiple autonomous vehicles in dynamic uncertain environments; adaptive flight control to enable autonomous agile flight and aerobatics and experimental and theoretical robust control. He is a fellow of the American Institute of Aeronautics and Astronautics (AIAA) and the Institute of Electrical and Electronics Engineers, and has received the 2020 AIAA Intelligent Systems Award. He earned a BS from the University of Toronto, and an MS and PhD from MIT.

Selected Publications

• Kim, D.K., Riemer, M., Liu, M., Foerster, J., Everett, M., Sun, C., Tesauro, G., How, J.P. (2022) Influencing long-term behavior in multiagent reinforcement learning. Advances in Neural Information Processing Systems 35, 18808-18821
• Sun, C., Kim, D.K., How, J.P (2022) ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation. The International Conference on Robotics and Automation (ICRA)
• Tagliabue, A., Kim, D.K.,Everett, M., How, J.P (2022) Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC, 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA, 2022, pp. 462-468, doi: 10.1109/ICRA46639.2022.9812122.
• Kim, D. K., Liu, M., Omidshafiei, S., Lopez-Cot, S., Riemer, M., Habibi, G., Tesauro, G., Mourad, S., Campbell, M., How, J. (2020). Learning Hierarchical Teaching Policies for Cooperative Agents. International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)

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