An Anytime Hierarchical Approach for Stochastic Task and Motion Planning

TL;DR

This paper introduces an anytime hierarchical method for stochastic task and motion planning that computes policies capable of handling multiple contingencies, ensuring probabilistic completeness and practical applicability in complex robotic tasks.

Contribution

The paper presents a novel integrated planning approach that effectively manages stochasticity and contingencies, with proven probabilistic completeness and anytime computation capabilities.

Findings

Algorithm is probabilistically complete.

Computes feasible policies in an anytime manner.

Demonstrated effectiveness on challenging problems.

Abstract

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 models are typically lossy and plans or policies computed using them can be inexecutable. These problems are exacerbated in stochastic situations where the robot needs to reason about and plan for multiple contingencies. We present a new approach for integrated task and motion planning in stochastic settings. In contrast to prior work in this direction, we show that our approach can effectively compute integrated task and motion policies whose branching structures encode agent behaviors that handle multiple execution-time contingencies. We prove that our algorithm is probabilistically complete and can compute feasible solution policies in an anytime fashion so that the probability of encountering an unresolved contingency decreases over time. Empirical results on a set of challenging problems show the utility and scope of our method.