Teleoperator Imitation with Continuous-time Safety
This addresses the challenge of enabling robots to safely and effectively learn complex skills from human demonstrations, with incremental improvements in safety and generalization.
The paper tackles the problem of imitating human teleoperators for robots in dynamic environments by proposing a motion learning technique based on contraction theory and sum-of-squares programming, which provides continuous-time safety guarantees and adapts to moving obstacles, showing favorable results on benchmark handwriting tasks.
Learning to effectively imitate human teleoperators, with generalization to unseen and dynamic environments, is a promising path to greater autonomy enabling robots to steadily acquire complex skills from supervision. We propose a new motion learning technique rooted in contraction theory and sum-of-squares programming for estimating a control law 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 continuous-time guarantees on the induced imitation behavior. Our method generalizes 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. We present an application of our framework for pick-and-place tasks in the presence of moving obstacles on a 7-DOF KUKA IIWA arm. The method compares favorably to other learning-from-demonstration approaches on benchmark handwriting imitation tasks.