Teleoperated Hexapod Robot for Imitation Learning Task Training To operate successfully in the unstructured environment of homes and small businesses, robots will be implemented by unskilled operators who cannot explicitly program their motions. Recently, imitation learning has been used to train robots that manipulate their environment based on pixeltoaction control. The robot actions are determined by camera inputs without programmed trajectories. Such training is often performed on robotic hardware which was not designed for imitation learning. This paper describes a new robot which is designed expressly to improve the training speed and ability of pixeltoaction policies. In particular, hexapod robot hardware is designed for teleoperation, thus improving correspondence and simplifying human control.

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.