FlashNeRD: Performance-First Contact-Rich Neural Robot Dynamics
Overview
FlashNeRD improves neural robot dynamics with speed, contact flexibility, and two-way object coupling. It is 55× faster than NeRD and enables efficient planning for complex tasks across five robots. DIAL-MPC allows robots to climb six platforms vs. one with fixed-contact NeRD, at half the planning time of analytical simulators. arXiv:2610.09130v1 Latest: FlashNeRD's dynamics model is up to 55× faster than an optimized NeRD and more accurate over long rollouts. Experiments across five robots show faster and more accurate dynamics, faster policy learning, and faster inference-time planning. With DIAL-MPC, a sampling-based MPC method, the robot climbs all six test platforms where fixed-contact NeRD manages one, at approximately half the analytical simulator's planning time. arXiv:2610.09130v1
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Reported quantity · speedup: 55 other · Basis not reported · Differing source assertions
“w faster and more accurate dynamics, faster policy learning, and faster inference-time planning. Across three robots, FlashNeRD's dynamics model is up to $55\times$ faster than an optimized NeRD and more accurate over long rollouts. This speedup extends to policy learning, where PPO trains an ANYmal locomotion policy in 34 seconds, $3.8\times$ faster than the analytical simulator and $2.7\times$ faster than an optimi”
Exact source · revision 1Source owner not reported
Reported quantity · speedup: 3.8 other · Basis not reported · Differing source assertions
“w faster and more accurate dynamics, faster policy learning, and faster inference-time planning. Across three robots, FlashNeRD's dynamics model is up to $55\times$ faster than an optimized NeRD and more accurate over long rollouts. This speedup extends to policy learning, where PPO trains an ANYmal locomotion policy in 34 seconds, $3.8\times$ faster than the analytical simulator and $2.7\times$ faster than an optimi”
Exact source · revision 1Source owner not reported
Reported quantity · speedup: 2.7 other · Basis not reported · Differing source assertions
“w faster and more accurate dynamics, faster policy learning, and faster inference-time planning. Across three robots, FlashNeRD's dynamics model is up to $55\times$ faster than an optimized NeRD and more accurate over long rollouts. This speedup extends to policy learning, where PPO trains an ANYmal locomotion policy in 34 seconds, $3.8\times$ faster than the analytical simulator and $2.7\times$ faster than an optimi”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsFlashNeRD: Performance-First Contact-Rich Neural Robot Dynamics
This paper presents FlashNeRD, a neural robot dynamics model that addresses key limitations of existing approaches by introducing a parallel streaming architecture, flexible contact encoding, and two-way coupling with simulated objects. Experiments show significant speedups and accuracy improvements in dynamics prediction, policy learning, and planning across multiple robots.
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