Bayesian Preference Elicitation: Human-In-The-Loop Optimization of An Active Prosthesis
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911470648819712 |
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| author | Taddei, Sophia Koppen, Wouter Alfio, Eligia Nuzzo, Stefano Flynn, Louis Diaz, Maria Alejandra Gonzalez, Sebastian Rojas Dhaene, Tom De Pauw, Kevin Couckuyt, Ivo Verstraten, Tom |
| author_facet | Taddei, Sophia Koppen, Wouter Alfio, Eligia Nuzzo, Stefano Flynn, Louis Diaz, Maria Alejandra Gonzalez, Sebastian Rojas Dhaene, Tom De Pauw, Kevin Couckuyt, Ivo Verstraten, Tom |
| contents | Tuning active prostheses for people with amputation is time-consuming and relies on metrics that may not fully reflect user needs. We introduce a human-in-the-loop optimization (HILO) approach that leverages direct user preferences to personalize a standard four-parameter prosthesis controller efficiently. Our method employs preference-based Multiobjective Bayesian Optimization that uses a state-or-the-art acquisition function especially designed for preference learning, and includes two algorithmic variants: a discrete version (\textit{EUBO-LineCoSpar}), and a continuous version (\textit{BPE4Prost}). Simulation results on benchmark functions and real-application trials demonstrate efficient convergence, robust preference elicitation, and measurable biomechanical improvements, illustrating the potential of preference-driven tuning for user-centered prosthesis control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22922 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bayesian Preference Elicitation: Human-In-The-Loop Optimization of An Active Prosthesis Taddei, Sophia Koppen, Wouter Alfio, Eligia Nuzzo, Stefano Flynn, Louis Diaz, Maria Alejandra Gonzalez, Sebastian Rojas Dhaene, Tom De Pauw, Kevin Couckuyt, Ivo Verstraten, Tom Robotics Tuning active prostheses for people with amputation is time-consuming and relies on metrics that may not fully reflect user needs. We introduce a human-in-the-loop optimization (HILO) approach that leverages direct user preferences to personalize a standard four-parameter prosthesis controller efficiently. Our method employs preference-based Multiobjective Bayesian Optimization that uses a state-or-the-art acquisition function especially designed for preference learning, and includes two algorithmic variants: a discrete version (\textit{EUBO-LineCoSpar}), and a continuous version (\textit{BPE4Prost}). Simulation results on benchmark functions and real-application trials demonstrate efficient convergence, robust preference elicitation, and measurable biomechanical improvements, illustrating the potential of preference-driven tuning for user-centered prosthesis control. |
| title | Bayesian Preference Elicitation: Human-In-The-Loop Optimization of An Active Prosthesis |
| topic | Robotics |
| url | https://arxiv.org/abs/2602.22922 |