Autonomy Talks - Wilko Schwarting: Learning & Control for Interactions in Mixed Environments

Autonomy Talks - 16/08/2021

Speaker: Dr. Wilko Schwarting, MIT/ISEE AI

Title: Learning and Control for Interactions in Mixed Human-Robot Environments

Abstract: Autonomous robots are on the verge of transforming our homes, factories, and roads. But to reap the tremendous benefits that robots offer to society, we must ensure that they can interact with humans seamlessly and safely. Towards this goal , my work focuses on giving robots the ability to autonomously acquire complexbehaviors, apply learned skills interactively considering the predicted intent of other agents, while explicitly modeling uncertainty throughout all levels of planning to ensure graceful recovery from prediction mismatch. In this talk, I will first present intelligent agents that learn how to reason about human behavior and peoples intentions. By incorporating social behavior, we can measure peoples willingness to cooperate when our interests are not aligned and negoti- ate through game-theoretic interactions. Second, I will show how in stochastic environ- ments with partial observability autonomous agents can leverage information gain and reason about others’ beliefs by combining game-theoretic and belief-space planning. I will present fast , scalable, and calibrated uncertainty estimation of neural networks and discuss how we can leverage uncertainty estimates in learned models for safe operation and efficient exploration in RL. Third, to learn complex behaviors, I will present reinforcement learning agents that learn competitive visual control policies through self-play in imagination. They learn complex skills from competition by imagining multi-agent inter- action sequences in the compact latent space ofa learnedworldmodel . These approaches enable autonomous agents to acquire complex skills from interaction, to perform robustly in uncertain environments, and to communicate intent through naturalistic behavior.

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