Regrasp Maps for Sequential Manipulation Planning

TL;DR

This paper introduces regrasp maps to enhance sequential manipulation planning by providing mode switch guesses, enabling faster and more robust solutions in cluttered environments with unknown regrasp locations.

Contribution

It presents a novel state space abstraction called regrasp maps that inform optimization-based TAMP solvers, improving efficiency in complex manipulation tasks.

Findings

Regrasp maps improve planning speed in cluttered environments.

The method handles unknown regrasp locations effectively.

Robustness increases through adaptive map refinement.

Abstract

We consider manipulation problems in constrained and cluttered settings, which require several regrasps at unknown locations. We propose to inform an optimization-based task and motion planning (TAMP) solver with possible regrasp areas and grasp sequences to speed up the search. Our main idea is to use a state space abstraction, a regrasp map, capturing the combinations of available grasps in different parts of the configuration space, and allowing us to provide the solver with guesses for the mode switches and additional constraints for the object placements. By interleaving the creation of regrasp maps, their adaptation based on failed refinements, and solving TAMP (sub)problems, we are able to provide a robust search method for challenging regrasp manipulation problems.