# LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning

## TL;DR

LEGO is a novel algorithm that enhances sampling-based motion planning by training a CVAE with strategically chosen samples from bottleneck regions, improving roadmap quality and planning success in complex environments.

## Contribution

LEGO introduces a new training approach for CVAE in motion planning, focusing on bottleneck and diverse samples, with formal guarantees and superior performance.

## Key findings

- Significant improvements over heuristics and learned baselines.
- Effective in complex obstacle environments and diverse planning problems.
- Formal performance guarantees for the proposed method.

## Abstract

We consider the problem of leveraging prior experience to generate roadmaps in sampling-based motion planning. A desirable roadmap is one that is sparse, allowing for fast search, with nodes spread out at key locations such that a low-cost feasible path exists. An increasingly popular approach is to learn a distribution of nodes that would produce such a roadmap. State-of-the-art is to train a conditional variational auto-encoder (CVAE) on the prior dataset with the shortest paths as target input. While this is quite effective on many problems, we show it can fail in the face of complex obstacle configurations or mismatch between training and testing. We present an algorithm LEGO that addresses these issues by training the CVAE with target samples that satisfy two important criteria. Firstly, these samples belong only to bottleneck regions along near-optimal paths that are otherwise difficult-to-sample with a uniform sampler. Secondly, these samples are spread out across diverse regions to maximize the likelihood of a feasible path existing. We formally define these properties and prove performance guarantees for LEGO. We extensively evaluate LEGO on a range of planning problems, including robot arm planning, and report significant gains over heuristics as well as learned baselines.