Learning to Influence Multi-Agent Interaction


We introduce a framework for multi-agent interaction that represents the low-level policies of non-stationary agents with high-level latent strategies.

When Humans Aren’t Optimal: Robots that Collaborate with Risk-Aware Humans


To create human-like robots, we need to understand how humans behave. We present a modeling approach enables robots to anticipate that humans will make suboptimal choices when risk and uncertainty are involved.

Controlling Assistive Robots with Learned Latent Actions


We want to make it easier for humans to teleoperate dexterous robots. We present a learning approach that embeds high-dimensional robot actions into an intuitive, human-controllable, and low-dimensional latent space.

Learning from My Partner’s Actions: Roles in Decentralized Robot Teams


When groups robots work together, their actions communicate valuable information. We introduce a collaborative learning and control strategy that enables robots to harness the information contained within their partner's actions.