  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.
