  The ability to adapt to uncertainties, recover from failures, and coordinate
between hand and fingers are essential sensorimotor skills for fully autonomous
robotic grasping. In this paper, we aim to study a unified feedback control
policy for generating the finger actions and the motion of hand to accomplish
seamlessly coordinated tasks of reaching, grasping and re-grasping. We proposed
a set of quantified metrics for task-orientated rewards to guide the policy
exploration, and we analyzed and demonstrated the effectiveness of each reward
term. To acquire a robust re-grasping motion, we deployed different initial
states in training to experience failures that the robot would encounter during
grasping due to inaccurate perception or disturbances. The performance of
learned policy is evaluated on three different tasks: grasping a static target,
grasping a dynamic target, and re-grasping. The quality of learned grasping
policy was evaluated based on success rates in different scenarios and the
recovery time from failures. The results indicate that the learned policy is
able to achieve stable grasps of a static or moving object. Moreover, the
policy can adapt to new environmental changes on the fly and execute
collision-free re-grasp after a failed attempt within a short recovery time
even in difficult configurations.
