Discovering relevant task spaces using inverse feedback control

Learning complex skills by repeating and generalizing expert behavior is a fundamental problem in robotics. However, the usual approaches do not answer the question of what are appropriate representations to generate motion for a specific task. Since it is time-consuming for a human expert to manually design the motion control representation for a task, we propose to uncover such structure from data-observed motion trajectories. Inspired by Inverse Optimal Control, we present a novel method to learn a latent value function, imitate and generalize demonstrated behavior, and discover a task relevant motion representation. We test our method, called Task Space Retrieval Using Inverse Feedback Control (TRIC), on several challenging high-dimensional tasks. TRIC learns the important control dimensions for the tasks from a few example movements and is able to robustly generalize to new situations.