用脚部传感器让高齿轮比人形机器人快速稳住步伐
Learning Bipedal Locomotion on Gear-Driven Humanoid Robot Using Foot-Mounted IMUs
- 用脚底惯性传感器替代扭矩传感器，简化控制复杂度
- 在非刚性地面和突变环境中实现快速稳定行走
- 适合研究高精度人形机器人运动控制的工程师
针对高齿轮比执行器人形机器人在仿真到现实强化学习中因复杂执行器动力学和缺乏扭矩传感器带来的挑战，本文提出一种新型强化学习框架，利用安装在脚部的惯性测量单元（IMUs）实现快速稳定。不同于复杂的执行器建模与系统辨识，该方法通过脚部IMU数据提升在复杂地形上的稳定性。同时，设计了针对该观测空间的对称数据增强策略及随机网络蒸馏技术，以增强粗糙地形上的双足行走能力。我们在微型人形机器人EVAL-03上进行了多种环境下的硬件实验，结果表明，该方法显著提升了在非刚性表面和突发环境变化下的快速稳定能力。
原文摘要 · Abstract (English)
Sim-to-real reinforcement learning (RL) for humanoid robots with high-gear ratio actuators remains challenging due to complex actuator dynamics and the absence of torque sensors. To address this, we propose a novel RL framework leveraging foot-mounted inertial measurement units (IMUs). Instead of pursuing detailed actuator modeling and system identification, we utilize foot-mounted IMU measurements to enhance rapid stabilization capabilities over challenging terrains. Additionally, we propose symmetric data augmentation dedicated to the proposed observation space and random network distillation to enhance bipedal locomotion learning over rough terrain. We validate our approach through hardware experiments on a miniature-sized humanoid EVAL-03 over a variety of environments. The experimental results demonstrate that our method improves rapid stabilization capabilities over non-rigid surfaces and sudden environmental transitions.
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