Guiding a Human Follower with Interaction Forces: Implications on Physical Human-Robot Interaction
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.
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
Mechanical and Aerospace Engineering
Keywords and Phrases
Human-robot interaction; intention detection; interaction forces; neural network
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
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01 Jan 2022
National Science Foundation, Grant 1843892