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This article introduces cuRoboV2-Fast, a GPU-accelerated motion planning system that significantly reduces latency for torque-limited robot tasks through B-spline optimization and differentiable inverse dynamics. By leveraging hardware like the RTX 5090 and Jetson AGX Orin, the method achieves up to 14.7x faster planning times compared to its predecessor while maintaining collision-free execution under changing payload conditions.
cuRoboV2-Fast: Fast Torque-Limited Collision-Free Motion Planning
arXiv:2611.06913v1 Announce Type: new Abstract: We present cuRoboV2-Fast, a GPU-accelerated planner that reduces both planning latency and execution time for collision-free, payload-aware, torque-limited motions. B-spline trajectory optimization reduces the required iterations, while accelerated robot computations and differentiable inverse dynamics reduce refinement cost and accommodate changing payloads. TimeOpt optimizes traversal speed along the fixed path under sampled velocity, acceleration, jerk, and torque constraints, exploiting local spline support to solve banded Newton systems efficiently on the GPU. With a 3,kg payload, cuRoboV2-Fast achieves 4.4 ms mean planning time on RTX 5090, 14.7x faster than cuRoboV2. Our method on Jetson AGX Orin plans faster than cuRoboV1 + TOPPRA on RTX 5090. A stereo-camera-based deployment on YAM achieves 5.93 s mean completed pick-and-drop cycles, 34.6% shorter than cuRoboV2. Code (Apache 2.0): www.github.com/NVlabs/curobo , Project Page: www.research.nvidia.com/labs/lpr/curobov2-fast .
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