Human-Robot Collaborative Object Transfer Using Human Motion Prediction Based on Cartesian Pose Dynamic Movement Primitives
Paper i proceeding, 2021

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.


Antonis Sidiropoulos

Aristotelio Panepistimio Thessalonikis

Yiannis Karayiannidis

Chalmers, Elektroteknik, System- och reglerteknik

Zoe Doulgeri

Aristotelio Panepistimio Thessalonikis

Proceedings - IEEE International Conference on Robotics and Automation

10504729 (ISSN)

Vol. 2021-May 3758-3764
9781728190778 (ISBN)

2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Xi'an, China,


Robotteknik och automation


Datorseende och robotik (autonoma system)



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