In this work, the problem of human-robot collaborative object transfer to unknown target poses is addressed. The desired pattern of the end-effector pose trajectory to a known target pose is encoded using DMPs (Dynamic Movement Primitives). During transportation of the object to new unknown targets, a DMP-based reference model and an EKF (Extended Kalman Filter) for estimating the target pose and time duration of the human's intended motion is proposed. A stability analysis of the overall scheme is provided. Experiments using a Kuka LWR4+ robot equipped with an ATI sensor at its end-effector validate its efficacy with respect to the required human effort and compare it with an admittance control scheme.
@article{arxiv.2104.03155,
title = {Human-robot collaborative object transfer using human motion prediction based on Cartesian pose Dynamic Movement Primitives},
author = {Antonis Sidiropoulos and Yiannis Karayiannidis and Zoe Doulgeri},
journal= {arXiv preprint arXiv:2104.03155},
year = {2021}
}