Global Tensor Motion Planning
This provides a scalable motion planning solution for robot learning tasks like distillation and imitation learning, though it appears incremental as it builds on existing sampling-based methods with tensorization.
The paper tackles the problem of batch motion planning for robot learning applications by introducing Global Tensor Motion Planning (GTMP), a sampling-based algorithm using only tensor operations that achieves probabilistic completeness and directly plans smooth splines without gradient optimization. Experiments on lidar-scanned occupancy maps and MotionBenchMarker dataset demonstrate GTMP's computational efficiency in batch planning compared to baselines.
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.