  Dexterous multi-fingered robotic hands have a formidable action space, yet
their morphological similarity to the human hand holds immense potential to
accelerate robot learning. We propose DexVIP, an approach to learn dexterous
robotic grasping from human-object interactions present in in-the-wild YouTube
videos. We do this by curating grasp images from human-object interaction
videos and imposing a prior over the agent's hand pose when learning to grasp
with deep reinforcement learning. A key advantage of our method is that the
learned policy is able to leverage free-form in-the-wild visual data. As a
result, it can easily scale to new objects, and it sidesteps the standard
practice of collecting human demonstrations in a lab -- a much more expensive
and indirect way to capture human expertise. Through experiments on 27 objects
with a 30-DoF simulated robot hand, we demonstrate that DexVIP compares
favorably to existing approaches that lack a hand pose prior or rely on
specialized tele-operation equipment to obtain human demonstrations, while also
being faster to train. Project page:
https://vision.cs.utexas.edu/projects/dexvip-dexterous-grasp-pose-prior
