Learning Deep Visuomotor Policies for Dexterous Hand Manipulation

Abstract

Multi-fingered dexterous hands are versatile and capable of acquiring a diverse set of skills such as grasping, in-hand manipulation, and tool use. To fully utilize their versatility in real-world scenarios, we require algorithms and policies that can control them using on-board sensing capabilities, without relying on external tracking or motion capture systems. Cameras and tactile sensors are the most widely used on-board sensors that do not require instrumentation of the world. In this work, we demonstrate an imitation learning based approach to train deep visuomotor policies for a variety of manipulation tasks with a simulated five fingered dexterous hand. These policies directly control the hand using high dimensional visual observations of the world and propreoceptive observations from the robot, and can be trained efficiently with a few hundred expert demonstration trajectories. We also find that using touch sensing information enables faster learning and better asymptotic performance for tasks with high degree of occlusions. Video demonstration of our results are available at: https://sites.google.com/view/hand-vil/