Force-velocity coupling limits human adaptation in physical human–robot interaction

In physical human-robot interaction, both humans and robots need to adapt to ensure synergetic behavior. This study investigated how humans respond to robots moving with different velocity profiles. In unconstrained human movements, velocity scales with the trajectory's curvature, i.e., moving fast at linear segments while slowing down at curved segments. Two experiments examined humans tracking a robot that traced an elliptic path with different velocity profiles, while instructed to minimize interaction forces. Results showed involuntary forces were higher when the robot moved with constant velocity or exaggerated the biological velocity-curvature scaling. Specifically, higher angular velocities in the robot were associated with greater tangential and normal forces. Experiment 1 tested whether biomechanical constraints caused these forces by reversing movement direction, but observed differences were small. Experiment 2 explored human adaptation across three practice sessions and found that interaction forces decreased for non-biological profiles only when real-time visual feedback was provided. The force-velocity modulations weakened, indicating that humans learned to predict and compensate for inertial forces. These findings highlight the need to consider human motor limitations and learning processes in physical interaction. The results have practical implications for collaborative and wearable robots where physical contact and coordination between humans and robots are critical.

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Publication Details

Journal
Scientific Reports
Published
2026-01-16
DOI
https://doi.org/10.1038/s41598-025-34959-4
Primary Topic
Motor Control and Adaptation
Type
article
Field-Weighted Citation Impact
0.00

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article

Force-velocity coupling limits human adaptation in physical human–robot interaction

Pauline Maurice, Mahdiar Edraki, Dagmar Sternad, Hélène Serre
Scientific Reports
Motor Control and Adaptation
article

Force-velocity coupling limits human adaptation in physical human–robot interaction

Pauline Maurice, Mahdiar Edraki, Dagmar Sternad, Hélène Serre
article en

Abstract

In physical human-robot interaction, both humans and robots need to adapt to ensure synergetic behavior. This study investigated how humans respond to robots moving with different velocity profiles. In unconstrained human movements, velocity scales with the trajectory's curvature, i.e., moving fast at linear segments while slowing down at curved segments. Two experiments examined humans tracking a robot that traced an elliptic path with different velocity profiles, while instructed to minimize interaction forces. Results showed involuntary forces were higher when the robot moved with constant velocity or exaggerated the biological velocity-curvature scaling. Specifically, higher angular velocities in the robot were associated with greater tangential and normal forces. Experiment 1 tested whether biomechanical constraints caused these forces by reversing movement direction, but observed differences were small. Experiment 2 explored human adaptation across three practice sessions and found that interaction forces decreased for non-biological profiles only when real-time visual feedback was provided. The force-velocity modulations weakened, indicating that humans learned to predict and compensate for inertial forces. These findings highlight the need to consider human motor limitations and learning processes in physical interaction. The results have practical implications for collaborative and wearable robots where physical contact and coordination between humans and robots are critical.

Scientific ReportsVol. 16(1)
Northeastern University (US), Centre National de la Recherche Scientifique (FR), Laboratoire Lorrain de Recherche en Informatique et ses Applications (FR), Université de Lorraine (FR)
Agence Nationale de la Recherche, National Institutes of Health
Openalex Percentile: Top 87%
Motor Control and Adaptation
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Force-velocity coupling limits human adaptation in physical human–robot interaction — Pauline Maurice, Mahdiar Edraki, et al. · Scientific Reports (2026) | TGRS Research Map | TGRS