Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning

TL;DR

This paper introduces Motion Memory, a method that leverages past motion planning experiences to significantly accelerate future planning tasks across various planners and problem types.

Contribution

It presents a simple, efficient approach to reuse past planning experiences to reduce planning time, applicable to different motion planners and problem classes.

Findings

Planning speed reduced by up to 89% with Motion Memory.

Effective across multiple motion planners and problem types.

Demonstrated on over 30,000 problem instances.

Abstract

When facing a new motion-planning problem, most motion planners solve it from scratch, e.g., via sampling and exploration or starting optimization from a straight-line path. However, most motion planners have to experience a variety of planning problems throughout their lifetimes, which are yet to be leveraged for future planning. In this paper, we present a simple but efficient method called Motion Memory, which allows different motion planners to accelerate future planning using past experiences. Treating existing motion planners as either a closed or open box, we present a variety of ways that Motion Memory can contribute to reduce the planning time when facing a new planning problem. We provide extensive experiment results with three different motion planners on three classes of planning problems with over 30,000 problem instances and show that planning speed can be significantly reduced by up to 89% with the proposed Motion Memory technique and with increasing past planning experiences.