公开(公告)号：US11420328B2

公开(公告)日：2022-08-23

申请号：US17425257

申请日：2020-01-31

Applicant: Google LLC

Inventor： Bachir El Khadir ,  Vikas Sindhwani ,  Jacob Varley

IPC: B25J13/00 ,  B25J9/16 ,  B25J9/02

Abstract: Learning to effectively imitate human teleoperators, even in unseen, dynamic environments is a promising path to greater autonomy, enabling robots to steadily acquire complex skills from supervision. Various motion generation techniques are described herein that are rooted in contraction theory and sum-of-squares programming for learning a dynamical systems control policy in the form of a polynomial vector field from a given set of demonstrations. Notably, this vector field is provably optimal for the problem of minimizing imitation loss while providing certain continuous-time guarantees on the induced imitation behavior. Techniques herein generalize to new initial and goal poses of the robot and can adapt in real time to dynamic obstacles during execution, with convergence to teleoperator behavior within a well-defined safety tube.