ArticleCommunications engineering2026
Model predictive game control for personalized and targeted interactive assistance.
Article in Communications engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
Who cites it
2 citing papers in PubMed.
- Advancing human sensorimotor research through modular robotics: the case of the HRX-1.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Review
- Partner-sourced haptic feedback rather than environmental inputs drives coordination improvement in human dyadic collaboration.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Contact robots are increasingly used to assist humans in physical training and manufacturing tasks. However, their effectiveness is currently limited as control methods focus on system performance without considering the upcoming human user's control. Here, we present a differential game-based controller for contact robots ensuring optimal interaction by predicting human motor control over their finite planning horizon. Using this model-predictive game (MPG) controller, we investigated human-robot co-adaptation in experiments, demonstrating that: (a) MPG interaction remains stable while reducing human effort; (b) the robot adapts to humans, identifying time-consistent individual interaction behaviors; (c) humans adapt to the robot, and their behavior can be modulated through an assistance meta-parameter adjusting the robot's propensity to minimize human effort. These findings indicate that humans understand and adapt to a partner's control strategy aligning with game theory principles. Furthermore, the assistance meta-parameter's ability to guide humans toward specific interaction behaviors enables versatile robot-assisted systems for physical training and rehabilitation.
Identifiers
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Registered trials
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.