Learning Bipedal Walking for Humanoids with Current Feedback

TL;DR

This paper introduces a reinforcement learning approach that leverages current feedback to effectively transfer bipedal walking policies from simulation to real humanoid robots, addressing the sim2real gap.

Contribution

It presents a novel method using current feedback and targeted dynamics randomization to achieve zero-shot sim2real transfer for humanoid walking without complex network architectures.

Findings

Successful deployment of RL policy on HRP-5P robot for bipedal walking.

Demonstrated robustness against uneven terrain compared to traditional controllers.

Eliminated need for memory-based networks by using feedforward architecture.

Abstract

Recent advances in deep reinforcement learning (RL) based techniques combined with training in simulation have offered a new approach to developing robust controllers for legged robots. However, the application of such approaches to real hardware has largely been limited to quadrupedal robots with direct-drive actuators and light-weight bipedal robots with low gear-ratio transmission systems. Application to real, life-sized humanoid robots has been less common arguably due to a large sim2real gap. In this paper, we present an approach for effectively overcoming the sim2real gap issue for humanoid robots arising from inaccurate torque-tracking at the actuator level. Our key idea is to utilize the current feedback from the actuators on the real robot, after training the policy in a simulation environment artificially degraded with poor torque-tracking. Our approach successfully trains a unified, end-to-end policy in simulation that can be deployed on a real HRP-5P humanoid robot to achieve bipedal locomotion. Through ablations, we also show that a feedforward policy architecture combined with targeted dynamics randomization is sufficient for zero-shot sim2real success, thus eliminating the need for computationally expensive, memory-based network architectures. Finally, we validate the robustness of the proposed RL policy by comparing its performance against a conventional model-based controller for walking on uneven terrain with the real robot.