# Efficient Path Planning In Manipulation Planning Problems by Actively Reusing Validation Effort

## TL;DR

This paper introduces a method for manipulation path planning that decomposes collision checks to enable active reuse of previous planning efforts across multiple queries, significantly reducing initial solution times.

## Contribution

It proposes a novel decomposition of collision checking in manipulation planning, facilitating multiquery reuse and active effort minimization in path planning algorithms.

## Key findings

- Reusing collision check information reduces initial planning time by up to 50%.
- The approach outperforms traditional planners in multiquery scenarios.
- Decomposition enables knowledge transfer across different problem instances.

## Abstract

The path planning problems arising in manipulation planning and in task and motion planning settings are typically repetitive: the same manipulator moves in a space that only changes slightly. Despite this potential for reuse of information, few planners fully exploit the available information. To better enable this reuse, we decompose the collision checking into reusable, and non-reusable parts. We then treat the sequences of path planning problems in manipulation planning as a multiquery path planning problem. This allows the usage of planners that actively minimize planning effort over multiple queries, and by doing so, actively reuse previous knowledge. We implement this approach in EIRM* and effort ordered LazyPRM*, and benchmark it on multiple simulated robotic examples. Further, we show that the approach of decomposing collision checks additionally enables the reuse of the gained knowledge over multiple different instances of the same problem, i.e., in a multiquery manipulation planning scenario. The planners using the decomposed collision checking outperform the other planners in initial solution time by up to a factor of two while providing a similar solution quality.