  Humans excel in grasping objects through diverse and robust policies, many of
which are so probabilistically rare that exploration-based learning methods
hardly observe and learn. Inspired by the human learning process, we propose a
method to extract and exploit latent intents from demonstrations, and then
learn diverse and robust grasping policies through self-exploration. The
resulting policy can grasp challenging objects in various environments with an
off-the-shelf parallel gripper. The key component is a learned intention
estimator, which maps gripper pose and visual sensory to a set of sub-intents
covering important phases of the grasping movement. Sub-intents can be used to
build an intrinsic reward to guide policy learning. The learned policy
demonstrates remarkable zero-shot generalization from simulation to the real
world while retaining its robustness against states that have never been
encountered during training, novel objects such as protractors and user
manuals, and environments such as the cluttered conveyor.
