Learning from Demonstration Based on a Mechanism to Utilize an Objectxe2x80x99s Invisibility This paper proposes a novel visual Learning from Demonstration (LfD) system that teaches robots to learn tasks in which an object is put into another object, stacked, or transported by a tool. In such tasks, observation targets become invisible owing to occlusion or frameout. The proposed xe2x80x9cVisual Hierarchybased Function Estimator (HiFes)xe2x80x9d is inspired by the knowledge derived from the field of psychology that uses the visual hierarchy relationships to estimate the changes in observation targets. HiFes employs a mechanism to interpolate the features when the targets are unrecognizable and state variables are incalculable directly. This method facilitated visual learning of complex state changes between multiple targets, in which the target becomes invisible or has a long period of invisibility, difficult for conventional learning methods. The proposed method was implemented in a lifesized humanoid robot and was evaluated in learning based on demonstration experiments. The results demonstrated the effectiveness of our approach.

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.