Motion planning is integral to robotics applications such as autonomous
driving, surgical robots, and industrial manipulators. Existing planning
methods lack scalability to higher-dimensional spaces, while recent learning
based planners have shown promise in accelerating sampling-based motion
planners (SMP) but lack generalizability to out-of-distribution environments.
To address this, we present a novel approach, Vector Quantized-Motion Planning
Transformers (VQ-MPT) that overcomes the key generalization and scaling
drawbacks of previous learning-based methods. VQ-MPT consists of two stages.
Stage 1 is a Vector Quantized-Variational AutoEncoder model that learns to
represent the planning space using a finite number of sampling distributions,
and stage 2 is an Auto-Regressive model that constructs a sampling region for
SMPs by selecting from the learned sampling distribution sets. By splitting
large planning spaces into discrete sets and selectively choosing the sampling
regions, our planner pairs well with out-of-the-box SMPs, generating
near-optimal paths faster than without VQ-MPT's aid. It is generalizable in
that it can be applied to systems of varying complexities, from 2D planar to
14D bi-manual robots with diverse environment representations, including
costmaps and point clouds. Trained VQ-MPT models generalize to environments
unseen during training and achieve higher success rates than previous methods.

本论文提出了一种名为矢量量化运动规划转换器（VQ-MPT）的新方法，它克服了以往基于学习的方法的主要限制，包括无法扩展到更高维度空间和缺乏对分布之外环境的通用性，使其能够应用于各种复杂性系统，实现了较高的成功率和更快的路径规划。