We present a new constraint-based framework for task and motion planning (TMP). Our
approach is extensible, probabilistically complete, and offers improved performance and …

We present a review and reformulation of manifold constrained sampling-based motion
planning within a unifying framework, IMACS (implicit manifold configuration space). IMACS …

This paper presents a general-purpose formulation of a large class of discrete-time planning
problems, with hybrid state and control-spaces, as factored transition systems. Factoring …

This paper studies the underlying combinatorial structure of a class of object rearrangement
problems, which appear frequently in applications. The problems involve multiple, similar …

We present a learning-based approach to prove infeasibility of kinematic motion planning
problems. Sampling-based motion planners are effective in high-dimensional spaces but …