Joint space and task space control are the two dominant action modes for controlling robot arms within the robot learning literature. Actions in joint space provide precise control over the robot's pose, but tend to suffer from inefficient training; actions in task space boast data-efficient training but sacrifice the ability to perform tasks in confined spaces due to limited control over the full joint configuration. This work analyses the criteria for designing action spaces for robot manipulation and introduces ER (End-effector Redundancy), a novel action space formulation that, by addressing the redundancies present in the manipulator, aims to combine the advantages of both joint and task spaces, offering fine-grained comprehensive control with overactuated robot arms whilst achieving highly efficient robot learning. We present two implementations of ER, ERAngle (ERA) and ERJoint (ERJ), and we show that ERJ in particular demonstrates superior performance across multiple settings, especially when precise control over the robot configuration is required. We validate our results both in simulated and real robotic environments.

通过分析机器人操作的标准，介绍了ER（末端执行器冗余）这一新的操作空间形式，旨在综合利用关节空间和任务空间的优势，在具备超定驱动机械臂的情况下提供精细全面的控制，并在高效机器人学习方面取得了卓越表现。通过模拟和真实机器人环境验证了ERJ在多种设置下特别是在需要对机器人配置进行精确控制的情况下的优越性能。

机器人学习中的冗余感知动作空间