外观
论文arXiv 2610.0913022 天前
FlashNeRD：性能优先的富接触神经机器人动力学
原文链接：FlashNeRD: Performance-First Contact-Rich Neural Robot Dynamics（arXiv 2610.09130 · 发布日期：2026-10-06）
FlashNeRD: Performance-First Contact-Rich Neural Robot Dynamics
FlashNeRD用并行流式架构、任意接触编码和双向耦合，改进NeRD并在五个机器人实验中验证加速与精度。
核心要点
- 动机是NeRD提速有限、接触点固定，且不能与被操作物体双向耦合。
- 方法采用并行流式架构、任意位置接触编码，并与解析求解器物体双向耦合。
- 五个机器人实验显示动力学更快更准，并带来更快的策略学习和推理时规划。
- 在三个机器人上模型最高比优化NeRD快55倍，长时滚动预测更准确。
验证范围与局限：验证范围为摘要所述五个机器人实验，以及ANYmal运动、平台攀爬和方块重定向任务；未说明真机实验。
英文摘要 (Abstract)
Compared with analytical physics, learned dynamics models promise robot simulation that is faster, inherently differentiable, and easily adaptable to real data. Neural Robot Dynamics (NeRD) pursues this by keeping collision detection analytical and replacing a simulator's numerical dynamics for the robot with a learned model. Three limitations remain. NeRD offers little speedup over the simulator it learned from, accepts contact only at predefined points, and has no two-way coupling with objects it manipulates. FlashNeRD removes all three with a parallel streaming architecture that makes each prediction faster and more accurate, an encoder that accepts contacts wherever they occur, and two-way coupling with objects simulated by analytical solvers. Experiments across five robots show 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 optimized NeRD. 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. Cube-reorientation policies trained with FlashNeRD complete within 2% of the simulator-trained policy's target count.
作者：Mohammadmehdi Ataei, Mohammad Amin Nabian, Jie Xu, Miles Macklin, Yashraj Narang, Eric Heiden, Octi Zhang, Yunrong Guo, Ruben D'Sa, Oliver Hennigh
来源
本页事实对应的公开页面，以原文为准。