Pushing the Boundaries of Asymptotic Optimality in Integrated Task and Motion Planning

Integrated task and motion planning problems describe a multi-modal state space, which is often abstracted as set of smooth manifolds that are connected via sets transitions states. One approach to solving such sample reachable states in each the manifolds, while simultaneously sampling transition Prior work has shown order achieve asymptotically optimal (AO) solutions for piecewise-smooth problems, it sufficient double connection radius required AO sampling-based planning. This was under assumption themselves smooth. The current builds upon this result demonstrates use same standard Furthermore, studies case non-smooth boundary points valid frequently practice, when gripper grasps an object. paper generalizes notion clearance typically assumed include individual, potentially It asymptotic optimality retained generalized regime.