用强化学习让并联机构单腿机器人学会高效跳跃
Learning to Hop for a Single-Legged Robot with Parallel Mechanism
- 用历史反馈编码解决跳跃时空中阶段过长带来的欠驱动问题
- 通过简化串行构型模拟并联结构，提升训练效率
- 适合对机器人动态控制与仿真-现实迁移感兴趣的读者
本文将强化学习应用于具有并联机构的高动态跳跃系统，以提升其性能。由于并联机构具有复杂的运动学约束和闭环结构，难以精确仿真。同时，跳跃任务存在空中阶段过长及奖励稀疏的问题。为此，提出一种学习框架，通过编码长期历史反馈来应对空中阶段导致的欠驱动问题。该框架引入简化串行构型替代直接模拟并联结构，以避免复杂仿真；并设计力矩级转换机制，实现并联-串行结构转换，缓解仿真到现实的迁移问题。通过仿真与硬件实验验证了该框架的有效性。
原文摘要 · Abstract (English)
This work presents the application of reinforcement learning to improve the performance of a highly dynamic hopping system with a parallel mechanism. Unlike serial mechanisms, parallel mechanisms can not be accurately simulated due to the complexity of their kinematic constraints and closed-loop structures. Besides, learning to hop suffers from prolonged aerial phase and the sparse nature of the rewards. To address them, we propose a learning framework to encode long-history feedback to account for the under-actuation brought by the prolonged aerial phase. In the proposed framework, we also introduce a simplified serial configuration for the parallel design to avoid directly simulating parallel structure during the training. A torque-level conversion is designed to deal with the parallel-serial conversion to handle the sim-to-real issue. Simulation and hardware experiments have been conducted to validate this framework.
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