Automated Discovery and Learning of Complex Movement Behaviours
In order to create truly autonomous physical robots, understand the underlying principles behind human movement, or tell narratives in animated films and interactive games, it is necessary to synthesize movement behaviours with the same wide variety, richness and complexity observed in humans and other animals. Moreover, these behaviours should be discovered automatically from only a few core principles, and not be a result of extensive manual engineering or a mimicking of demonstrations. In this talk at the intersection of robotics, computer graphics and biomechanics, I will show work on novel trajectory and policy optimization methods that give rise to a range of behaviours such getting up, climbing, moving objects, hand manipulation, acrobatics, and various cooperative actions involving multiple characters all in a single system. The resulting movements can be used to successfully control a physical bipedal robot and coupled with detailed models of human physiology, motions that match real human motion can be produced de novo, giving the predictive power to conduct virtual biomechanics experiments. The approach is fully automatic, based on general neural network policy representations and does not require domain knowledge specific to each behaviour, pre-existing examples or motion capture data. Although discovery and learning are computationally-expensive and rely on cloud and GPU computing, the interactive animation can run in real-time on any hardware once the controllers are learned.