Learning basketball dribbling skills using trajectory optimization and deep reinforcement learning

Basketball is one of the world's most popular sports because of the agility and speed demonstrated by the players. This agility and speed makes designing controllers to realize robust control of basketball skills a challenge for physics-based character animation. The highly dynamic behaviors and precise manipulation of the ball that occur in the game are difficult to reproduce for simulated players. In this paper, we present an approach for learning robust basketball dribbling controllers from motion capture data. Our system decouples a basketball controller into locomotion control and arm control components and learns each component separately. To achieve robust control of the ball, we develop an efficient pipeline based on trajectory optimization and deep reinforcement learning and learn non-linear arm control policies. We also present a technique for learning skills and the transition between skills simultaneously. Our system is capable of learning robust controllers for various basketball dribbling skills, such as dribbling between the legs and crossover moves. The resulting control graphs enable a simulated player to perform transitions between these skills and respond to user interaction.