research
Task Planning under Uncertainty
Developing planning abstractions that allow robots to efficiently perform complex tasks despite uncertainty and incomplete knowledge about their environment, e.g. missing objects and unknown locations, while performing other task-relevant actions in large, uncertain environments.
Multi-Robot Coordination
Coordinating teams of robots to efficiently divide search efforts and complete complex tasks while acting concurrently in large, uncertain environments. Planning and coordination for heterogenous teams (drones, rovers, humanoids) whose skills, efficiency, and capabilities may vary.
Introspection for Long-Horizon Planning
Developing introspection for robots—the ability to look back at past decisions and imagine how alternative behaviors could have led to better outcomes via counterfactual reasoning without first-hand experience of failure.
Autonomous Navigation and Adaptation
Enabling robots to efficiently navigate unknown environments via integrated planning and learning methods. Developing techniques for reliable deployment-time adaptation, allowing robots to monitor their own performance and select the best adaptation strategies during deployment for reliable navigation in novel environments.
Foundation Models for Planning
Combining the commonsense world knowledge of LLMs, VLMs and emerging foundation models with classical model-based planning for effective and reliable long-horizon decision-making.