论文标题
使用增强学习的人形机器人的反应性步进：应用在外骨骼上恢复到阿塔兰特的恢复
Reactive Stepping for Humanoid Robots using Reinforcement Learning: Application to Standing Push Recovery on the Exoskeleton Atalante
论文作者
论文摘要
最先进的增强学习能够在模拟中学习双皮德机器人的多功能运动，平衡和推动回收能力。但是，现实差距大多被忽略了，模拟结果几乎不会转移到真实硬件上。在实践中，它是不成功的，因为物理学过度简化，硬件限制被忽略，或者不能保证规律性，并且可能会发生意外的危险运动。本文提出了一个强化学习框架，能够学习稳健的站立式恢复，以平稳转移到现实，仅提供瞬时的本体感受观察。通过结合原始的终止条件和政策平滑度调节，我们使用没有记忆或明确历史的政策实现了稳定的学习，SIM到实现的转移和安全性。然后使用奖励工程来洞悉如何保持平衡。我们在下LIMB医学外骨骼Atalante中展示了它在现实中的表现。
State-of-the-art reinforcement learning is now able to learn versatile locomotion, balancing and push-recovery capabilities for bipedal robots in simulation. Yet, the reality gap has mostly been overlooked and the simulated results hardly transfer to real hardware. Either it is unsuccessful in practice because the physics is over-simplified and hardware limitations are ignored, or regularity is not guaranteed, and unexpected hazardous motions can occur. This paper presents a reinforcement learning framework capable of learning robust standing push recovery for bipedal robots that smoothly transfer to reality, providing only instantaneous proprioceptive observations. By combining original termination conditions and policy smoothness conditioning, we achieve stable learning, sim-to-real transfer and safety using a policy without memory nor explicit history. Reward engineering is then used to give insights into how to keep balance. We demonstrate its performance in reality on the lower-limb medical exoskeleton Atalante.
