面向全身操作与运动技能的统一策略的高效学习
Efficient Learning of A Unified Policy For Whole-body Manipulation and Locomotion Skills
AI总结：
本文提出将机械臂显式运动学模型集成到强化学习框架中，以缓解四足移动操作任务中的局部最优问题，并在DeepRobotics X20与Unitree Z1平台上验证了统一策略的高效学习效果。
摘要：
为四足机器人配备机械臂可提供独特的移动操作能力，使多种实际应用成为可能。这种集成形成了一个更复杂的系统，在建模与控制方面难度增加。强化学习（RL）通过交互学习最优控制策略，为解决这些挑战提供了一种有前景的方案。然而，RL方法在探索运动与操作任务的大规模解空间时，往往容易陷入局部最优。为克服这些局限，我们提出一种新方法，将机械臂的显式运动学模型集成到RL框架中。该集成提供关于身体姿态到机械臂工作空间映射的反馈，引导RL探索过程并有效缓解局部最优问题。我们的算法已成功部署在配备Unitree Z1机械臂的DeepRobotics X20四足机器人上，大量实验结果表明该方法具有优越的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
