Policy Learning for Social Robot-Led Physiotherapy
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916949145944064 |
|---|---|
| author | Bettosi, Carl Ballie, Lynne Shenkin, Susan Romeo, Marta |
| author_facet | Bettosi, Carl Ballie, Lynne Shenkin, Susan Romeo, Marta |
| contents | Social robots offer a promising solution for autonomously guiding patients through physiotherapy exercise sessions, but effective deployment requires advanced decision-making to adapt to patient needs. A key challenge is the scarcity of patient behavior data for developing robust policies. To address this, we engaged 33 expert healthcare practitioners as patient proxies, using their interactions with our robot to inform a patient behavior model capable of generating exercise performance metrics and subjective scores on perceived exertion. We trained a reinforcement learning-based policy in simulation, demonstrating that it can adapt exercise instructions to individual exertion tolerances and fluctuating performance, while also being applicable to patients at different recovery stages with varying exercise plans. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11297 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Policy Learning for Social Robot-Led Physiotherapy Bettosi, Carl Ballie, Lynne Shenkin, Susan Romeo, Marta Robotics Artificial Intelligence Social robots offer a promising solution for autonomously guiding patients through physiotherapy exercise sessions, but effective deployment requires advanced decision-making to adapt to patient needs. A key challenge is the scarcity of patient behavior data for developing robust policies. To address this, we engaged 33 expert healthcare practitioners as patient proxies, using their interactions with our robot to inform a patient behavior model capable of generating exercise performance metrics and subjective scores on perceived exertion. We trained a reinforcement learning-based policy in simulation, demonstrating that it can adapt exercise instructions to individual exertion tolerances and fluctuating performance, while also being applicable to patients at different recovery stages with varying exercise plans. |
| title | Policy Learning for Social Robot-Led Physiotherapy |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.11297 |