用深度强化学习让低成本四足机器人学会复杂方向移动。
Training Directional Locomotion for Quadrupedal Low-Cost Robotic Systems via Deep Reinforcement Learning
- 通过随机化行进方向促进探索，提升转向与前进能力。
- 实测可完成频繁转弯和长直线路径，无需人工干预。
- 适合低成本机器人在真实环境中的自主运动训练。
本文提出一种在真实世界中训练低成本四足机器人实现方向性行走的深度强化学习方法。通过在每轮训练中将机器人的初始朝向设置为当前航向加上正态分布随机值，激发对动作-状态转移的有效探索，使策略同时掌握前进与路径调整能力。该方法使机器人持续在训练平台内运动，显著减少人工干预和手动重置需求。在自研低成本四足机器人上的真实实验表明，所提方法能成功完成包含频繁转向和长直行的验证测试；而采用其他训练方式的机器人仅能完成直线行走，在需要转向时失败。
原文摘要 · Abstract (English)
In this work we present Deep Reinforcement Learning (DRL) training of directional locomotion for low-cost quadrupedal robots in the real world. In particular, we exploit randomization of heading that the robot must follow to foster exploration of action-state transitions most useful for learning both forward locomotion as well as course adjustments. Changing the heading in episode resets to current yaw plus a random value drawn from a normal distribution yields policies able to follow complex trajectories involving frequent turns in both directions as well as long straight-line stretches. By repeatedly changing the heading, this method keeps the robot moving within the training platform and thus reduces human involvement and need for manual resets during the training. Real world experiments on a custom-built, low-cost quadruped demonstrate the efficacy of our method with the robot successfully navigating all validation tests. When trained with other approaches, the robot only succeeds in forward locomotion test and fails when turning is required.
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