Contingency planning, wherein an agent generates a set of possible plans conditioned on
the outcome of an uncertain event, is an increasingly popular way for robots to act under …

Game-theoretic motion planners are a potent solution for controlling systems of multiple
highly interactive robots. Most existing game-theoretic planners unrealistically assume a …

In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level,
abstract planning and reasoning in conjunction with motion planning. However, abstract …

Multirobot symbolic planning (MSP) aims at computing plans, each in the form of a
sequence of actions, for a team of robots to achieve their individual goals while minimizing …

Humans have a remarkable ability to accurately reason about future events, including the
behaviors and states of mind of other agents. Consider driving a car through a busy …

We investigate the problem of cooperative multi-robot planning in unknown environments,
which is important in numerous applications in robotics. The research community has been …

This thesis is concerned with robot motion planning in dynamic, cluttered, and uncertain
environments. Successful and efficient robot operation in such environments requires …

We investigate the problem of cooperative multi-robot planning in unknown environments,
which is important in numerous applications in robotics. The research community has been …

We present a novel method for handling uncertainty about the intentions of non-ego players
in trajectory games, with application to motion planning for autonomous vehicles. Our …

Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for
optimal robot navigation with safety guarantees. Previous work on optimal motion planning …