Publication
Global Tensor Motion Planning
A. T. Le; K. Hansel; J. Carvalho; J. Urain; A. Biess; G. Chalvatzaki; Jan Peters
In: CoRL 2024 Workshop on Differentiable Optimization Everywhere. Conference on Robot Learning (CoRL-2024), CoRL, 2024.
Abstract
Batch planning is increasingly necessary to quickly produce diverse and high-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.