A Systematic Review of Hybrid dynamical system models for human–robot interaction: Methods, Architectures, and Future Research Directions
Bibliographic record
Abstract
Hybrid dynamical systems have emerged as a powerful mathematical and computational framework for modeling complex interactions between continuous and discrete processes in human–robot interaction (HRI). These systems enable the integration of physical dynamics, decision-making logic, and adaptive control, which are essential for safe and efficient collaboration between humans and robots in dynamic environments. This paper presents a comprehensive systematic review of hybrid dynamical system models for HRI, focusing on methodologies, architectures, and future research directions. The review synthesizes recent advances between 2018 and 2025, examining key modeling paradigms such as switched systems, hybrid automata, and learning-based hybrid frameworks. The findings highlight the growing convergence of control theory, machine learning, and cognitive modeling in HRI systems, emphasizing improvements in safety, adaptability, and real-time responsiveness. The paper contributes a structured analysis of 30 representative studies, identifies research gaps in scalability, interpretability, and robustness, and outlines future directions including AI-integrated hybrid models and secure human-aware robotic systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".