用深度强化学习让六足机器人在复杂地形自然稳定行走
Learning Natural and Robust Hexapod Locomotion over Complex Terrains via Motion Priors based on Deep Reinforcement Learning
- 基于运动先验生成优化数据，指导机器人学习自然步态
- 实机验证中无需视觉信息即实现复杂地形稳健行走
- 首个在真实六足机器人上实现复杂地形行走的强化学习方案
多足机器人凭借多条腿与环境的交互，在复杂地形中具备更强稳定性。然而，如何在更大的动作探索空间中有效协调多条腿以生成自然且鲁棒的运动，仍是关键挑战。本文提出一种基于运动先验的方法，成功将深度强化学习应用于真实六足机器人。通过生成优化的运动先验数据集，并训练对抗性判别器以引导机器人学习自然步态。所学策略最终成功迁移至真实六足机器人，在无视觉信息条件下实现了自然的步态模式和显著的鲁棒性，可在复杂地形中稳定行走。这是首次在真实六足机器人上实现基于强化学习的复杂地形行走。
原文摘要 · Abstract (English)
Multi-legged robots offer enhanced stability to navigate complex terrains with their multiple legs interacting with the environment. However, how to effectively coordinate the multiple legs in a larger action exploration space to generate natural and robust movements is a key issue. In this paper, we introduce a motion prior-based approach, successfully applying deep reinforcement learning algorithms to a real hexapod robot. We generate a dataset of optimized motion priors, and train an adversarial discriminator based on the priors to guide the hexapod robot to learn natural gaits. The learned policy is then successfully transferred to a real hexapod robot, and demonstrate natural gait patterns and remarkable robustness without visual information in complex terrains. This is the first time that a reinforcement learning controller has been used to achieve complex terrain walking on a real hexapod robot.
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