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.

Influencing Leading and Following in Human-Robot Teams

So much of our lives centers around coordinating in groups. As robots become increasingly integrated into society, they should be able to similarly coordinate well with human groups. However, influencing groups of people is challenging. Our goal is to develop a framework that enables robots to model and influence human groups that is scalable with the number of human agents.