Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive
results in recent years. While many studies in RL focus on varying the observation space or
reward model, few efforts focused on the choice of action space (eg joint or end-effector
space, position, velocity, etc.). However, studies in robot motion control indicate that
choosing an action space that conforms to the characteristics of the task can simplify
exploration and improve robustness to disturbances. This paper studies the effect of different …