Global Tensor Motion Planning

Abstract

Batch planning is increasingly necessary to quickly produce diverse and quality motion plans for downstream learning applications, such as distillation and imitation learning. This paper presents Global Tensor Motion Planning (GTMP) -- a sampling-based motion planning algorithm comprising only tensor operations. We introduce a novel discretization structure represented as a random multipartite graph, enabling efficient vectorized sampling, collision checking, and search. We provide a theoretical investigation showing that GTMP exhibits probabilistic completeness while supporting modern GPU/TPU. Additionally, by incorporating smooth structures into the multipartite graph, GTMP directly plans smooth splines without requiring gradient-based optimization. Experiments on lidar-scanned occupancy maps and the MotionBenchMarker dataset demonstrate GTMP's computation efficiency in batch planning compared to baselines, underscoring GTMP's potential as a robust, scalable planner for diverse applications and large-scale robot learning tasks.

8 pages, 3 figures. Accepted at IEEE Robotics and Automation Letters 2025

References in corpus (6)

• Continuous-Time Gaussian Process Motion Planning via Probabilistic Inference
• Batch Informed Trees (BIT*): Informed Asymptotically Optimal Anytime Search
• Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics
• Multimodal Trajectory Optimization for Motion Planning
• MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets
• Learning Implicit Priors for Motion Optimization