Guiding a Human Follower with Interaction Forces: Implications on Physical Human-Robot Interaction

Author

George L. Holmes
Keyri Moreno Bonnett
Amy Costa
Devin Michael Burns, Missouri University of Science and TechnologyFollow
Yun Seong Song, Missouri University of Science and TechnologyFollow

Abstract

This work challenges the common assumption in physical human-robot interaction (pHRI) that the movement intention of a human user can be simply modeled with dynamic equations relating forces to movements, regardless of the user. Studies in physical human-human interaction (pHHI) suggest that interaction forces carry sophisticated information that reveals motor skills and roles in the partnership and even promotes adaptation and motor learning. In this view, simple force-displacement equations often used in pHRI studies may not be sufficient. To test this, this work measured and analyzed the interaction forces (F) between two humans as the leader guided the blindfolded follower on a randomly chosen path. The actual trajectory of the follower was transformed to the velocity commands (V) that would allow a hypothetical robot follower to track the same trajectory. Then, possible analytical relationships between F and V were obtained using neural network training. Results suggest that while F helps predict V, the relationship is not straightforward, that seemingly irrelevant components of F may be important, that force-velocity relationships are unique to each human follower, and that human neural control of movement may affect the prediction of the movement intent. It is suggested that user-specific, stereotype-free controllers may more accurately decode human intent in pHRI.

Recommended Citation

Holmes, G. L., Bonnett, K. M., Costa, A., Burns, D. M., & Song, Y. S. (2022). Guiding a Human Follower with Interaction Forces: Implications on Physical Human-Robot Interaction. Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics Institute of Electrical and Electronics Engineers.

The definitive version is available at https://doi.org/10.1109/BioRob52689.2022.9925337

Department(s)

Psychological Science

Second Department

Mechanical and Aerospace Engineering

Comments

National Science Foundation, Grant 1843892

Keywords and Phrases

Human-robot interaction; intention detection; interaction forces; neural network

International Standard Book Number (ISBN)

978-166545849-8

International Standard Serial Number (ISSN)

2155-1774

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2022