Abstract Purpose of Review Planning collision-free paths for multiple robots is important for
real-world multi-robot systems and has been studied as an optimization problem on graphs …

Deep reinforcement learning (DRL) has gained great success by learning directly from high-
dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of …

Reinforcement learning and symbolic planning have both been used to build intelligent
autonomous agents. Reinforcement learning relies on learning from interactions with real …

Robot planning in partially observable domains is difficult, because a robot needs to
estimate the current state and plan actions at the same time. When the domain includes …

Existing autonomous robot navigation systems allow robots to move from one point to
another in a collision-free manner. However, when facing new environments, these systems …

We aim for mobile robots to function in a variety of common human environments. Such
robots need to be able to reason about the locations of previously unseen target objects …

Autonomous mobile robots deployed in outdoor environments must reason about different
types of terrain for both safety (eg, prefer dirt over mud) and deployer preferences (eg, prefer …

Robots need task planning algorithms to sequence actions toward accomplishing goals that
are impossible through individual actions. Off-the-shelf task planners can be used by …

People are proficient at communicating their intentions in order to avoid conflicts when
navigating in narrow, crowded environments. Mobile robots, on the other hand, often lack …

Task and motion planning (TAMP) algorithms aim to help robots achieve task-level goals,
while maintaining motion-level feasibility. This paper focuses on TAMP domains that involve …