Abstract

Deep learning has resulted in remarkable breakthroughs in fields such as speech recognition, computer vision, natural language processing, and protein structure prediction. Robotics has proved to be much more challenging as there are no pre-existing repositories of behavior to draw upon; rather the robot has to learn from its own trial and error in its own specific body, and it has to generalize and adapt. I believe that the most promising approach for this is to train robot skills in simulation and then transfer them to the real world. I will show multiple examples of skills – legged locomotion (quadruped and humanoid), navigation, and dexterous manipulation such as in-hand rotation and twisting caps off bottles – acquired in this paradigm. Along the way, we developed “Rapid Motor Adaptation”, a method for adaptive control in the framework of deep reinforcement learning. Looking to the future, I believe that there are multiple insights from the development of motor skills in children that are relevant to robotics; I will sketch some examples and partial results. While we are many years away from having robots with the skills of a five year old, progress in the last few years has been remarkable and substantial.