Learning Implicit Sampling Distributions for Motion Planning

TL;DR

This paper introduces a learning-based approach to adaptively generate sampling distributions for motion planning, leveraging past experiences to improve efficiency across different environments.

Contribution

It presents a policy-search method that learns implicit sampling distributions from previous searches, enhancing motion planning performance in new environments.

Findings

Reduced collision checks in experiments with a 7DOF robot arm.

Fewer nodes expanded compared to baseline methods.

Improved planning efficiency across various tasks.

Abstract

Sampling-based motion planners have experienced much success due to their ability to efficiently and evenly explore the state space. However, for many tasks, it may be more efficient to not uniformly explore the state space, especially when there is prior information about its structure. Previous methods have attempted to modify the sampling distribution using hand selected heuristics that can work well for specific environments but not universally. In this paper, a policy- search based method is presented as an adaptive way to learn implicit sampling distributions for different environments. It utilizes information from past searches in similar environments to generate better distributions in novel environments, thus reducing overall computational cost. Our method can be incor- porated with a variety of sampling-based planners to improve performance. Our approach is validated on a number of tasks, including a 7DOF robot arm, showing marked improvement in number of collision checks as well as number of nodes expanded compared with baseline methods.