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However, sustaining long-term user engagement remains a major challenge for SRs, largely due to their limited understanding of human mental states. Accordingly, we leverage a recently introduced mathematical dynamic model of human perception, cognition, and decision-making for behavioral control of SRs. By identifying the parameters of this model and deploying it within a model-based behavioral steering system, SRs can autonomously adapt their actions to evolving user mental states, enhancing long-term engagement and personalization. To achieve this, we introduce the first integration of a systems-theoretic cognitive model into a closed-loop predictive behavioral control framework for SRs, formulated as a constrained multi-objective optimization problem that enables transparent, cognition-aware adaptation. In experiments with\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{10}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    participants interacting with a Nao robot across three chess puzzle sessions (\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{45}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    to\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{90}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    minutes each), the identified model achieved a mean squared error (MSE) of\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{0.067}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    (i.e.,\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{1.675\\%}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    of the maximum possible MSE) in tracking beliefs, goals, and emotions of participants, and increased engagement by\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{16\\%}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    (\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{p = 0.009}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) compared to a model-free baseline. Post-interaction participant questionnaires further confirmed the perceived engagement and awareness of the model-based controller. Overall, the framework provides a practical pathway toward SRs that autonomously adapt to users in real time, sustain long-term engagement, and ultimately deliver more effective and personalized assistance in domains such as healthcare, education, and companionship.\n                  <\/jats:p>","DOI":"10.1007\/s10489-026-07100-9","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T09:05:26Z","timestamp":1773047126000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging systems and control theory for social robotics: a model-based behavioral control approach to human-robot interaction"],"prefix":"10.1007","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8650-8707","authenticated-orcid":false,"given":"Maria L.","family":"Mor\u00e3o Patr\u00edcio","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anahita","family":"Jamshidnejad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"7100_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/J.HLPT.2021.100544","volume":"10","author":"HL Bradwell","year":"2021","unstructured":"Bradwell HL, Noury GEA, Edwards KJ, Winnington R, Thill S, Jones RB (2021) Design recommendations for socially assistive robots for health and social care based on a large scale analysis of stakeholder positions: Social robot design recommendations. 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