  Dexterous robotic hands are appealing for their agility and human-like
morphology, yet their high degree of freedom makes learning to manipulate
challenging. We introduce an approach for learning dexterous grasping. Our key
idea is to embed an object-centric visual affordance model within a deep
reinforcement learning loop to learn grasping policies that favor the same
object regions favored by people. Unlike traditional approaches that learn from
human demonstration trajectories (e.g., hand joint sequences captured with a
glove), the proposed prior is object-centric and image-based, allowing the
agent to anticipate useful affordance regions for objects unseen during policy
learning. We demonstrate our idea with a 30-DoF five-fingered robotic hand
simulator on 40 objects from two datasets, where it successfully and
efficiently learns policies for stable functional grasps. Our affordance-guided
policies are significantly more effective, generalize better to novel objects,
train 3 X faster than the baselines, and are more robust to noisy sensor
readings and actuation. Our work offers a step towards manipulation agents that
learn by watching how people use objects, without requiring state and action
information about the human body. Project website:
http://vision.cs.utexas.edu/projects/graff-dexterous-affordance-grasp
