Physically Assistive Robots require personalized behaviors to ensure user safety and comfort. However, traditional preference learning methods, like exhaustive pairwise comparisons, cause substantial physical and cognitive fatigue for users with severe 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. 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, occupational therapists confirmed the generated policies are safe and accurately reflect user preferences.

翻译：物理辅助机器人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（例如详尽的成对比较）会给严重运动障碍用户带来巨大的生理和认知疲劳。为解决此问题，我们提出一个低负担的离线框架，可将非结构化的自然语言反馈直接转化为确定性机器人控制策略。为安全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的大语言模型。这种临床推理将主观的用户反应解码为明确的生理与心理需求，再映射为透明的决策树。在部署前，自动化的“大语言模型作为评判者”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，对10名瘫痪成年人进行了系统验证。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负担。此外，职业治疗师确认生成的策略安全可靠，并能准确反映用户偏好。