Reinforcement and Imitation Learning for Diverse Visuomotor Skills

We propose a general model-free deep reinforcement learning method and apply it to robotic manipulation tasks. Our approach leverages a small amount of demonstration data to assist a reinforcement learning agent. We train end-to-end visuomotor policies to learn a direct mapping from RGB camera inputs to joint velocities. We demonstrate that the same agent, trained with the same algorithm, can solve a wide variety of visuomotor tasks, where engineering a scripted controller can be laborious. In experiments, our reinforcement and imitation agent achieves significantly better performances than agents trained with reinforcement learning or imitation learning alone. We also illustrate that these policies, trained with large visual and dynamics variations, can achieve preliminary successes in zero-shot sim2real transfer.