用自然语言反馈训练助残机器人，降低用户认知负担。
Low-Burden LLM-Based Preference Learning: Personalizing Assistive Robots from Natural Language Feedback for Users with Paralysis
- 通过LLM解析用户口语反馈，生成可执行的控制策略
- 10名瘫痪用户参与测试，工作负荷显著低于传统方法
- 适合残障用户及康复治疗师，安全可解释
物理辅助机器人需个性化行为以保障用户安全与舒适。但传统偏好学习方法（如全面成对比较）会给严重运动障碍用户带来巨大身体与认知负担。为此，我们提出一种低负担、离线的框架，将非结构化自然语言反馈直接转化为确定性机器人控制策略。为安全弥合人类语言模糊性与机器人代码之间的差距，该流程基于职业治疗实践框架（Occupational Therapy Practice Framework）的LLM，解码主观用户反应为明确的生理与心理需求，并映射为透明决策树。部署前，由自动化“LLM作为裁判”验证代码结构安全性。在10名瘫痪成人参与的模拟餐食准备实验中，自然语言方法显著降低用户工作负荷。此外，职业治疗师确认生成策略安全且准确反映用户偏好。
原文摘要 · Abstract (English)
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.
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