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Control Meets Learning Seminar

Wednesday, May 12, 2021
9:00am to 10:00am
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Online Event
Learning-based Planning and Control: Opportunities and Challenges
Jonathan How, Professor of Aeronautics and Astronautics, Department of Aeronautics and Astronautics Aerospace Controls Laboratory, Massachusetts Institute of Technology,

Machine learning-based techniques have recently revolutionized nearly every aspect of autonomy. In particular, deep reinforcement learning (RL) has rapidly become a powerful alternative to classical model-based approaches to decision-making, planning, and control. Despite the well-publicized successes of deep RL, its adoption in complex and/or safety-critical tasks at scale and in real-world settings is hindered by several key issues, including high sample complexity in large-scale problems, limited transferability, and lack of robustness guarantees. This talk explores our recently developed solutions that address these fundamental challenges for both single and multiagent RL. In addition, this talk highlights the complementary role that classical model-based techniques can play in synergy with data-driven methods in overcoming these issues. Real experiments with ground and aerial robots will be used to illustrate the effectiveness of the proposed techniques. The talk will conclude with an assessment of the state of the art and highlight important avenues for future research.

For more information, please contact Jolene Brink by email at jbrink@caltech.edu or visit Control Meets Learning Website.