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:
this http URL

本研究介绍了一种在深度强化学习循环中嵌入面向对象视觉助力模型的方法，以学习优先选择与人类喜欢的对象区域相同的抓握策略，实现对物体的灵活抓握能力。通过 40 个物体的实验，表明该方法可以显著提高抓握策略效能，泛化能力较强，比普通基线方法的训练速度更快，且更能适应噪声传感器。