Before revision:  We combine imitation learning and reinforcement learning in order to grasp unseen objects and handle the continuous 6D action space, where expert demonstrations are obtained from a joint motion and grasp planner. We introduce a goal-auxiliary actor-critic algorithm , which uses grasping goal prediction as an auxiliary task to facilitate policy learning.
After revision:  We combine imitation learning and reinforcement learning and introduce a goal-auxiliary actor-critic algorithm , which uses grasping goal prediction as an auxiliary task to facilitate policy learning.