Human-robot collaborative object transfer using human motion prediction based on Dynamic Movement Primitives

Abstract

This work focuses on the prediction of the human's motion in a collaborative human-robot object transfer with the aim of assisting the human and minimizing his/her effort. The desired pattern of motion is learned from a human demonstration and is encoded with a DMP (Dynamic Movement Primitive). During the object transfer to unknown targets, a model reference with a DMP-based control input and an EKF-based (Extended Kalman Filter) observer for predicting the target and temporal scaling is used. Global boundedness under the emergence of bounded forces with bounded energy is proved. The object dynamics are assumed known. The validation of the proposed approach is performed through experiments using a Kuka LWR4+ robot equipped with an ATI sensor at its end-effector.