Human-Robot Collaborative Object Transfer Using Human Motion Prediction Based on Cartesian Pose Dynamic Movement Primitives
Paper in 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

Aristotle University of Thessaloniki

Yiannis Karayiannidis

Chalmers, Electrical Engineering, Systems and control

Zoe Doulgeri

Aristotle University of Thessaloniki

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,

Subject Categories


Control Engineering

Computer Vision and Robotics (Autonomous Systems)



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