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.

翻译：运动规划是自主驾驶、手术机器人和工业机械臂等机器人应用的核心环节。现有规划方法在更高维度空间缺乏可扩展性，而近期基于学习的规划器虽能加速基于采样的运动规划器（SMP）但泛化至分布外环境的能力不足。为此，我们提出一种新型方法——向量量化-运动规划Transformer（VQ-MPT），克服了先前基于学习方法的关键泛化与扩展缺陷。VQ-MPT包含两个阶段：阶段1采用向量量化变分自编码器模型，通过有限数量的采样分布学习规划空间的表征；阶段2采用自回归模型，从已学习的采样分布集合中选取构建SMP的采样区域。通过将大型规划空间分解为离散集合并选择性选取采样区域，我们的规划器能与现成的SMP高效协同，比未经VQ-MPT辅助时更快生成近优路径。该方法具备泛化能力，可应用于从二维平面到14维双臂机器人的不同复杂度系统，并适配包含代价地图与点云等多种环境表征。训练后的VQ-MPT模型能泛化至训练中未见的环境，且成功率优于先前方法。