DexV2A

Achieving human-like dexterity for common daily tasks with a robotic hand is a challenging task for reinforcement learning. To combat the high dimensionality of the state-action space, we propose pre-training policies on low-level vision tasks. Our system, called DexV2A, first trains a neural network on structural vision tasks like edge detection, center point estimation and surface normal estimation to embed useful visual features into the network weights. When this network is transferred to a dexterous manipulation policy, it offers an advantageous initialization for task learning. We conduct experiments on four diverse manipulation tasks with a 30-DoF dexterous robotic hand in simulation. We show that for the tasks of opening, closing, pouring, and stirring, DexV2A improves policy learning over policies trained without any visual pre-training. Our experimental results demonstrate the effectiveness of our approach, and emphasizes the potency of visual pre-training over learning via direct experience