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Control Meets Learning Seminar

Wednesday, February 3, 2021
9:00am to 10:00am
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Online Event
Safe, Interaction-Aware Decision Making and Control for Robot Autonomy
Marco Pavone, Associate Professor, Department of Aeronautics and Astronautics, Stanford University,

In this talk I will present a decision-making and control stack for human-robot interactions by using autonomous driving as a motivating example. Specifically, I will first discuss a data-driven approach for learning multimodal interaction dynamics between robot-driven and human-driven vehicles based on recent advances in deep generative modeling. Then, I will discuss how to incorporate such a learned interaction model into a real-time, interaction-aware decision-making framework. The framework is designed to be minimally interventional; in particular, by leveraging backward reachability analysis, it ensures safety even when other cars defy the robot's expectations without unduly sacrificing performance. I will present recent results from experiments on a full-scale steer-by-wire platform, validating the framework and providing practical insights. I will conclude the talk by providing an overview of related efforts from my group on infusing safety assurances in robot autonomy stacks equipped with learning-based components, with an emphasis on adding structure within robot learning via control-theoretical and formal methods.

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