On the Critical Role of Conventions in Adaptive Human-AI Collaboration
We learn convention-aware agents for adaptive multi-agent collaboration.
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.