  Vision-based grasping systems typically adopt an open-loop execution of a
planned grasp. This policy can fail due to many reasons, including ubiquitous
calibration error. Recovery from a failed grasp is further complicated by
visual occlusion, as the hand is usually occluding the vision sensor as it
attempts another open-loop regrasp. This work presents MAT, a tactile
closed-loop method capable of realizing grasps provided by a coarse initial
positioning of the hand above an object. Our algorithm is a deep reinforcement
learning (RL) policy optimized through the clipped surrogate objective within a
maximum entropy RL framework to balance exploitation and exploration. The
method utilizes tactile and proprioceptive information to act through both fine
finger motions and larger regrasp movements to execute stable grasps. A novel
curriculum of action motion magnitude makes learning more tractable and helps
turn common failure cases into successes. Careful selection of features that
exhibit small sim-to-real gaps enables this tactile grasping policy, trained
purely in simulation, to transfer well to real world environments without the
need for additional learning. Experimentally, this methodology improves over a
vision-only grasp success rate substantially on a multi-fingered robot hand.
When this methodology is used to realize grasps from coarse initial positions
provided by a vision-only planner, the system is made dramatically more robust
to calibration errors in the camera-robot transform.
