  Recent advances in on-policy reinforcement learning (RL) methods enabled
learning agents in virtual environments to master complex tasks with
high-dimensional and continuous observation and action spaces. However,
leveraging this family of algorithms in multi-fingered robotic grasping remains
a challenge due to large sim-to-real fidelity gaps and the high sample
complexity of on-policy RL algorithms. This work aims to bridge these gaps by
first reinforcement-learning a multi-fingered robotic grasping policy in
simulation that operates in the pixel space of the input: a single depth image.
Using a mapping from pixel space to Cartesian space according to the depth map,
this method transfers to the real world with high fidelity and introduces a
novel attention mechanism that substantially improves grasp success rate in
cluttered environments. Finally, the direct-generative nature of this method
allows learning of multi-fingered grasps that have flexible end-effector
positions, orientations and rotations, as well as all degrees of freedom of the
hand.
