公开(公告)号：US20220297303A1

公开(公告)日：2022-09-22

申请号：US17203296

申请日：2021-03-16

Applicant: X Development LLC

Inventor： Seyed Mohammad Khansari Zadeh ,  Eric Jang ,  Daniel Lam ,  Daniel Kappler ,  Matthew Bennice ,  Brent Austin ,  Yunfei Bai ,  Sergey Levine ,  Alexander Irpan ,  Nicolas Sievers ,  Chelsea Finn

IPC: B25J9/16 ,  B25J13/06

Abstract: Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.