OneShot Composition of VisionBased Skills from Demonstration We consider the problem of learning multistage visionbased tasks on a real robot from a single video of a human performing the task, while leveraging demonstration data of subtasks with other objects. This problem presents a number of major challenges. Video demonstrations without teleoperation are easy for humans to provide, but do not provide any direct supervision. Learning policies from raw pixels enables full generality but calls for large function approximators with many parameters to be learned. Finally, compound tasks can require impractical amounts of demonstration data, when treated as a monolithic skill. To address these challenges, we propose a method that learns both how to learn primitive behaviors from video demonstrations and how to dynamically compose these behaviors to perform multistage tasks by xe2x80x9cwatchingxe2x80x9d a human demonstrator. Our results on a simulated Sawyer robot and real PR2 robot illustrate our method for learning a variety of order fulfillment and kitchen serving tasks with novel objects and raw pixel inputs. Video results are linked at https:sites.google.comviewoneshothil.

Extending Policy from OneShot Learning through Coaching Humans generally teach their fellow collaborators to perform tasks through a small number of demonstrations, often followed by episodes of coaching that tune and refine the execution during practice. Adopting a similar framework for teaching robots through demonstrations makes teaching tasks highly intuitive and imitating the refinement of complex tasks through coaching improves the efficacy. Unlike traditional Learning from Demonstration (LfD) approaches which rely on multiple demonstrations to train a task, we present a novel oneshot learning from demonstration approach, augmented by coaching, to transfer the task from task expert to robot. The demonstration is automatically segmented into a sequence of a priori skills (the task policy) parametrized to match task goals. During practice, the robotic skills selfevaluate their performances and refine the task policy to locally optimize cumulative performance. Then, human coaching further refines the task policy to explore and globally optimize the net performance. Both the selfevaluation and coaching are implemented using reinforcement learning (RL) methods. The proposed approach is evaluated using the task of scooping and unscooping granular media. The selfevaluator of the scooping skill uses the realtime force signature and resistive force theory to minimize scooping resistance similar to how humans scoop. Coaching feedback focuses modifications to subdomains of the action space, using RL to converge to desired performance. Thus, the proposed method provides a framework for learning tasks from one demonstration and generalizing it using human feedback through coaching achieving a success rate of xe2x89x8890%.