Physically Assistive Robots (PARs) require personalized behaviors to ensure user safety and comfort. However, traditional preference learning methods, like exhaustive pairwise comparisons, cause severe physical and cognitive fatigue for users with profound motor impairments. To solve this, we propose a low-burden, offline framework that translates unstructured natural language feedback directly into deterministic robotic control policies. To safely bridge the gap between ambiguous human speech and robotic code, our pipeline uses Large Language Models (LLMs) grounded in the Occupational Therapy Practice Framework (OTPF). This clinical reasoning decodes subjective user reactions into explicit physical and psychological needs, which are then mapped into transparent decision trees. Before deployment, an automated "LLM-as-a-Judge" verifies the code's structural safety. We validated this system in a simulated meal preparation study with 10 adults with paralysis. Results show our natural language approach significantly reduces user workload compared to traditional baselines. Additionally, independent clinical experts confirmed the generated policies are safe and accurately reflect user preferences.

翻译：物理辅助机器人（PARs）需要个性化行为以确保用户安全与舒适。然而，传统的偏好学习方法（如详尽的成对比较）会导致严重运动障碍用户产生严重的身体和认知疲劳。为解决这一问题，我们提出了一种低负担的离线框架，该框架可直接将非结构化的自然语言反馈转化为确定性的机器人控制策略。为了安全地弥合模糊的人类语言与机器人代码之间的鸿沟，我们的流程采用基于职业治疗实践框架（OTPF）的大语言模型（LLMs）。这种临床推理将主观用户反应解码为明确的生理和心理需求，进而映射为透明的决策树。在部署前，自动化“LLM作为评判者”机制会验证代码的结构安全性。我们在一项模拟备餐研究中验证了该系统，共有10名瘫痪成年人参与。结果表明，与传统基线方法相比，我们的自然语言方法显著降低了用户工作负担。此外，独立临床专家确认生成的策略既安全又准确反映了用户偏好。