Automated Curriculum Design for High-dimensional Human Motor Learning
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914565379325952 |
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| author | Kamboj, Ankur Ranganathan, Rajiv Tan, Xiaobo Srivastava, Vaibhav |
| author_facet | Kamboj, Ankur Ranganathan, Rajiv Tan, Xiaobo Srivastava, Vaibhav |
| contents | Designing effective practice schedules for high-dimensional motor learning tasks remains a challenge, especially when skill states are unobservable and task performance may not reflect the true learning. We propose an automated curriculum design framework that combines a human motor learning model and personalized real-time skill estimation with Stochastic Nonlinear Model Predictive Control in \emph{de-novo} (novel) motor learning paradigms. We validated our framework both through simulations and human-subject studies (N = 36) using a hand exoskeleton. Our proposed approach accelerates skill acquisition by $\sim23\%$, and ${\sim17\%}$ when compared to a random curriculum and a performance heuristics-based curriculum, respectively. These significant gains in learning efficiency highlight the potential of model-based, individualized curricula for motor rehabilitation and complex skill training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14367 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automated Curriculum Design for High-dimensional Human Motor Learning Kamboj, Ankur Ranganathan, Rajiv Tan, Xiaobo Srivastava, Vaibhav Systems and Control Human-Computer Interaction Optimization and Control Designing effective practice schedules for high-dimensional motor learning tasks remains a challenge, especially when skill states are unobservable and task performance may not reflect the true learning. We propose an automated curriculum design framework that combines a human motor learning model and personalized real-time skill estimation with Stochastic Nonlinear Model Predictive Control in \emph{de-novo} (novel) motor learning paradigms. We validated our framework both through simulations and human-subject studies (N = 36) using a hand exoskeleton. Our proposed approach accelerates skill acquisition by $\sim23\%$, and ${\sim17\%}$ when compared to a random curriculum and a performance heuristics-based curriculum, respectively. These significant gains in learning efficiency highlight the potential of model-based, individualized curricula for motor rehabilitation and complex skill training. |
| title | Automated Curriculum Design for High-dimensional Human Motor Learning |
| topic | Systems and Control Human-Computer Interaction Optimization and Control |
| url | https://arxiv.org/abs/2605.14367 |