.png){kind=icon}
返回
Learning Bipedal Walking for Humanoids With Current Feedback
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3301175.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Robots
Legged locomotion
Humanoid robots
Torque
Foot
Actuators
Training
Deep learning
Reinforcement learning
Bipedal locomotion
humanoid robots
reinforcement learning
sim2real
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Resistance to allosteric SHP2 inhibition in FGFR-driven cancers through rapid feedback activation of FGFR.png)
OncotargetOAAI
OncotargetIF0
OAAI分享收藏Predicting Internet network distance with coordinates-based approachesProceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies
Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications SocietiesIF0
PREAI
分享收藏分享收藏
查看更多内容