# Policy Learning for Social Robot-Led Physiotherapy

arXiv: https://arxiv.org/abs/2509.11297 · HTML: https://mlsanity.com/paper/2509.11297/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 14 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Reinforcement Learning Approaches in Social Robotics](https://mlsanity.com/paper/2009.09689/) (2020)

## Abstract (arXiv)

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.