cuRoboV2-Fast: Fast Torque-Limited Collision-Free Motion Planning
Overview
This paper introduces cuRoboV2-Fast, a GPU-accelerated planner designed for fast, collision-free motion planning under torque limits. By combining B-spline trajectory optimization with differentiable inverse dynamics, the method reduces iteration counts and refinement costs. On an RTX 5090 GPU, it achieves a mean planning time of 4.4 ms with a 3 kg payload, which is 14.7 times faster than cuRoboV2. The system also demonstrates improved pick-and-drop cycle times (5.93 s) on the YAM platform using stereo cameras.
Generated from attributed reports · Updated 3 days ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · planning time: 4.4 other · Basis not reported
“timizes 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 deployme”
Exact source · revision 1Source owner not reported
Reported quantity · pick-and-drop cycles: 5.93 other · reported trials
“nt 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 .”
Exact source · revision 1Source owner not reported
Reported quantity · payload: 3 other · Basis not reported
“timizes 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 deployme”
Exact source · revision 1Source owner not reported
Reported quantity · planning time reduction factor: 14.7 other · Basis not reported
“timizes 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 deployme”
Exact source · revision 1Source owner not reported
Artifact availability · code: reported pending
“nt 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 .”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalcuRoboV2-Fast: Fast Torque-Limited Collision-Free Motion Planning
This paper introduces cuRoboV2-Fast, a GPU-accelerated planner designed for fast, collision-free motion planning under torque limits. By combining B-spline trajectory optimization with differentiable inverse dynamics, the method reduces iteration counts and refinement costs. On an RTX 5090 GPU, it achieves a mean planning time of 4.4 ms with a 3 kg payload, which is 14.7 times faster than cuRoboV2. The system also demonstrates improved pick-and-drop cycle times (5.93 s) on the YAM platform using stereo cameras.
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