  Many objects such as tools and household items can be used only if grasped in
a very specific way - grasped functionally. Often, a direct functional grasp is
not possible, though. We propose a method for learning a dexterous pre-grasp
manipulation policy to achieve human-like functional grasps using deep
reinforcement learning. We introduce a dense multi-component reward function
that enables learning a single policy, capable of dexterous pre-grasp
manipulation of novel instances of several known object categories with an
anthropomorphic hand. The policy is learned purely by means of reinforcement
learning from scratch, without any expert demonstrations, and implicitly learns
to reposition and reorient objects of complex shapes to achieve given
functional grasps. Learning is done on a single GPU in less than three hours.

Robotics