Evidence map›Paper›PMID 41545443›Full record

ArticleScientific reports2026

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

Mahdiar Edraki, Hélène Serré, Pauline Maurice, Dagmar Sternad

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mahdiar EdrakiDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, USA. edraki.m@northeastern.edu.
Hélène SerréDepartment of Biology, Northeastern University, Boston, USA.
Pauline MauriceUniversité de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France.
Dagmar SternadDepartment of Biology, Northeastern University, Boston, USA.

Funding

Predictability in complex object controlR37HD087089 · NICHD · NORTHEASTERN UNIVERSITY · PI Dagmar Sternad · 2022 to 2026
$3.0M
Agence Nationale de la Recherche ANR-20-CE33-004NICHD NIH HHS R37 HD087089NIH HHS NIH-R37-HD087089
6 · The paper itself

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.

Indexed as

Adaptation, PhysiologicalRoboticsAdultBiomechanical PhenomenaFemaleHumansMaleMovement

Identifiers

PMID41545443
PMCPMC12887034

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.