Path Planning for Manipulation using Experience-driven Random Trees

TL;DR

This paper introduces experience-driven random trees (ERT) and ERTConnect, novel motion planners that effectively generalize prior experiences to new, dissimilar manipulation tasks, outperforming existing experience-based methods in success rate and efficiency.

Contribution

The work proposes a new approach that decomposes and adapts prior experiences for planning in unfamiliar scenarios, enabling better generalization and efficiency.

Findings

ERT outperforms state-of-the-art experience-based planners.

Single experience can significantly improve planning success and speed.

The methods are implemented in the Open Motion Planning Library.

Abstract

Robotic systems may frequently come across similar manipulation planning problems that result in similar motion plans. Instead of planning each problem from scratch, it is preferable to leverage previously computed motion plans, i.e., experiences, to ease the planning. Different approaches have been proposed to exploit prior information on novel task instances. These methods, however, rely on a vast repertoire of experiences and fail when none relates closely to the current problem. Thus, an open challenge is the ability to generalise prior experiences to task instances that do not necessarily resemble the prior. This work tackles the above challenge with the proposition that experiences are "decomposable" and "malleable", i.e., parts of an experience are suitable to relevantly explore the connectivity of the robot-task space even in non-experienced regions. Two new planners result from this insight: experience-driven random trees (ERT) and its bi-directional version ERTConnect. These planners adopt a tree sampling-based strategy that incrementally extracts and modulates parts of a single path experience to compose a valid motion plan. We demonstrate our method on task instances that significantly differ from the prior experiences, and compare with related state-of-the-art experience-based planners. While their repairing strategies fail to generalise priors of tens of experiences, our planner, with a single experience, significantly outperforms them in both success rate and planning time. Our planners are implemented and freely available in the Open Motion Planning Library.