# Motions in Microseconds via Vectorized Sampling-Based Planning

arXiv: https://arxiv.org/abs/2309.14545 · HTML: https://mlsanity.com/paper/2309.14545/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2023.

## Built on by (most cited first, 17 of 17)

- [Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness](https://mlsanity.com/paper/2403.10454/) (2024)
- [End-to-End Dexterous Arm-Hand VLA Policies via Shared Autonomy: VR Teleoperation Augmented by Autonomous Hand VLA Policy for Efficient Data Collection](https://mlsanity.com/paper/2511.00139/) (2025)
- [Constraint-Preserving Data Generation for Visuomotor Policy Learning](https://mlsanity.com/paper/2508.03944/) (2025)
- [Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Trees](https://mlsanity.com/paper/2508.21001/) (2025)
- [Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments](https://mlsanity.com/paper/2409.18411/) (2024)
- [Global Tensor Motion Planning](https://mlsanity.com/paper/2411.19393/) (2024)
- [Vectorized Online POMDP Planning](https://mlsanity.com/paper/2510.27191/) (2025)
- [MatterDoor: Sampling Zero-shot Spatio-semantic Priors using Generative Models](https://mlsanity.com/paper/2510.11014/) (2025)
- [Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities](https://mlsanity.com/paper/2603.24318/) (2026)
- [Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models](https://mlsanity.com/paper/2604.07084/) (2026)
- [SkipVLA: Skipping VLA Steps with Classical Planning for Fast Robot Manipulation](https://mlsanity.com/paper/2609.20648/) (2026)
- [Model Tensor Planning](https://mlsanity.com/paper/2505.01059/) (2025)
- [Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning](https://mlsanity.com/paper/2508.08983/) (2025)
- [Parallel Heuristic Search as Inference for Actor-Critic Reinforcement Learning Models](https://mlsanity.com/paper/2509.25402/) (2025)
- [Constant-Time Planning for Chaining Collision-free Motion to Manipulation Behaviors](https://mlsanity.com/paper/2512.00939/) (2025)
- [Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving](https://mlsanity.com/paper/2602.08334/) (2026)
- [vla.simd: Efficient CPU Inference for Language-Conditioned Manipulation](https://mlsanity.com/paper/2609.24274/) (2026)

## Abstract (arXiv)

Modern sampling-based motion planning algorithms typically take between hundreds of milliseconds to dozens of seconds to find collision-free motions for high degree-of-freedom problems. This paper presents performance improvements of more than 500x over the state-of-the-art, bringing planning times into the range of microseconds and solution rates into the range of kilohertz, without specialized hardware. Our key insight is how to exploit fine-grained parallelism within sampling-based planners, providing generality-preserving algorithmic improvements to any such planner and significantly accelerating critical subroutines, such as forward kinematics and collision checking. We demonstrate our approach over a diverse set of challenging, realistic problems for complex robots ranging from 7 to 14 degrees-of-freedom. Moreover, we show that our approach does not require high-power hardware by also evaluating on a low-power single-board computer. The planning speeds demonstrated are fast enough to reside in the range of control frequencies and open up new avenues of motion planning research.