外观
论文arXiv 2609.132892 个月前
通过精细化执行器建模与自适应命令调度扩展四足运动速度极限
原文链接：Extending the Speed Limit of Quadrupedal Locomotion via Refined Actuator Modeling and Adaptive Command Scheduling（arXiv 2609.13289 · 发布日期：2026-08-21）
Extending the Speed Limit of Quadrupedal Locomotion via Refined Actuator Modeling and Adaptive Command Scheduling
该框架通过精细执行器模型和自适应命令调度缩小高速四足运动的仿真到现实差距，并在BP2上验证高速表现。
核心要点
- 高速四足运动中执行器接近物理极限，非线性会放大仿真到现实差距。
- 方法显式建模高速电压耦合与磁饱和，并结合两阶段课程和ACS强化学习。
- BP2实验达到跑步机13.2 m/s、户外11.65 m/s，并声称刷新四足机器人速度纪录。
- 结果表明精确执行器建模可避免非物理策略利用，ACS提升鲁棒性且不牺牲性能。
验证范围与局限：验证范围为36.5 kg四足机器人BlackPanther2在跑步机和户外的高速运动实验。
英文摘要 (Abstract)
Achieving high-speed locomotion in quadrupedal robots remains highly challenging, as actuators operate near their physical limits and exhibit pronounced nonlinearities. However, many existing methods neglect actuator nonlinearities and physical constraints during training, leading to a significant sim-to-real gap under highly dynamic motions and limiting achievable performance. To address this issue, we propose a high-speed locomotion framework that reduces sim-to-real discrepancies and stabilizes learning over a wide command distribution. A refined actuator model explicitly captures high-speed voltage coupling and magnetic saturation, enabling a more accurate representation of the torque-speed envelope. In addition, a reinforcement learning framework incorporating a two-stage curriculum and adaptive command scheduling (ACS) ensures stable training. Experiments on the 36.5 kg quadruped BlackPanther2 (BP2) demonstrate speeds of up to 13.2 m/s on a treadmill and 11.65 m/s outdoors, establishing a new state-of-the-art and, to the best of our knowledge, a world record for quadrupedal robot locomotion. The results further highlight the importance of accurate actuator modeling in preventing non-physical policy exploitation, and show that ACS improves robustness without sacrificing performance.
作者：Yucheng Tao, Shaowen Cheng, Guorong Lan, Yanyan Yuan, Yongbin Jin, Hongtao Wang
来源
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