robotics
The Robot Plans Around Torque in 4.4 Milliseconds
cuRoboV2-Fast combines B-spline optimization, differentiable inverse dynamics and traversal-time optimization for payload-aware motion.
Summary
cuRoboV2-Fast combines B-spline optimization, differentiable inverse dynamics and traversal-time optimization for payload-aware motion.
With a three-kilogram payload on an RTX 5090, the authors report 4.4 milliseconds mean planning time, 14.7 times faster than cuRoboV2. Their Jetson AGX Orin implementation also plans faster than a cuRoboV1-plus-TOPPRA baseline running on an RTX 5090. In stereo-camera deployment on YAM, mean completed pick-and-drop cycles take 5.93 seconds, 34.6 percent shorter than cuRoboV2. The results concern the tested planner, hardware, payload and tasks; they do not establish collision-free performance for every robot or scene.
Why it matters
cuRoboV2-Fast combines B-spline optimization, differentiable inverse dynamics and traversal-time optimization for payload-aware motion.
Limits and context
- The results concern the tested planner, hardware, payload and tasks; they do not establish collision-free performance for every robot or scene.
Key claims
cuRoboV2-Fast combines B-spline optimization, differentiable inverse dynamics and traversal-time optimization for payload-aware motion.
Qualification: The results concern the tested planner, hardware, payload and tasks; they do not establish collision-free performance for every robot or scene.
Evidence: source-2026-11-05-005
Sources
- arXiv preprint 2611.06913, original v1arXiv · primary research
Corrections
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