将机械臂运动学模型融入强化学习，提升四足机器人的协同操作与行走能力。
Efficient Learning of A Unified Policy For Whole-body Manipulation and Locomotion Skills
- 在强化学习中嵌入机械臂显式运动学模型，引导探索方向
- 在X20机器人上实现复杂任务，有效避免局部最优
- 适合需要协调运动与操作的机器人研发人员
为四足机器人配备机械臂可赋予其独特的运操协同能力，拓展实际应用。但这种集成使系统更复杂，建模与控制难度显著增加。强化学习（RL）通过交互学习最优控制策略，为解决该问题提供新路径。然而，传统RL在探索大规模解空间时易陷入局部最优。为此，本文提出一种新方法：将机械臂的显式运动学模型引入强化学习框架，提供躯体姿态到机械臂工作空间的映射反馈，引导探索过程，有效缓解局部最优问题。算法已在配备Unitree Z1机械臂的DeepRobotics X20四足机器人上成功部署，大量实验验证了该方法的优越性能。
原文摘要 · Abstract (English)
Equipping quadruped robots with manipulators provides unique loco-manipulation capabilities, enabling diverse practical applications. This integration creates a more complex system that has increased difficulties in modeling and control. Reinforcement learning (RL) offers a promising solution to address these challenges by learning optimal control policies through interaction. Nevertheless, RL methods often struggle with local optima when exploring large solution spaces for motion and manipulation tasks. To overcome these limitations, we propose a novel approach that integrates an explicit kinematic model of the manipulator into the RL framework. This integration provides feedback on the mapping of the body postures to the manipulator's workspace, guiding the RL exploration process and effectively mitigating the local optima issue. Our algorithm has been successfully deployed on a DeepRobotics X20 quadruped robot equipped with a Unitree Z1 manipulator, and extensive experimental results demonstrate the superior performance of this approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。