  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.
