用神经符号框架让助老机器人动态调整伸展指导
StretchBot: A Neuro-Symbolic Framework for Adaptive Guidance with Assistive Robots
- 融合多模态感知与大模型推理，实现上下文自适应
- 试点显示自适应引导更贴合用户状态，流畅性仍逊于固定流程
- 适合关注人机交互适应性的研究者与康复机器人开发者
助老机器人在家庭和医疗环境中具有提升身体健康的潜力，例如通过指导用户进行伸展或康复训练。然而，现有系统多为固定脚本，难以根据用户状态、环境背景和交互动态进行调整。本文提出StretchBot，一种结合多模态感知与知识图谱驱动的大语言模型推理的神经符号机器人教练，可在短时伸展训练中实现上下文感知的动态调整，同时保持训练结构化。为补充系统描述，我们对三位参与者进行了探索性试点对比，比较了脚本式与自适应引导的效果。结果表明，自适应条件提升了感知到的适应性和情境相关性，而脚本式引导在流畅性和可预测性方面仍具优势。这些发现初步验证了结构化行动知识有助于在具身辅助交互中锚定基于语言模型的自适应行为，同时也强调需要更大规模、长期的研究来评估系统的鲁棒性、泛化能力及长期用户体验。
原文摘要 · Abstract (English)
Assistive robots have growing potential to support physical wellbeing in home and healthcare settings, for example, by guiding users through stretching or rehabilitation routines. However, existing systems remain largely scripted, which limits their ability to adapt to user state, environmental context, and interaction dynamics. In this work, we present StretchBot, a hybrid neuro-symbolic robotic coach for adaptive assistive guidance. The system combines multimodal perception with knowledge-graph-grounded large language model reasoning to support context-aware adjustments during short stretching sessions while maintaining a structured routine. To complement the system description, we report an exploratory pilot comparison between scripted and adaptive guidance with three participants. The pilot findings suggest that the adaptive condition improved perceived adaptability and contextual relevance, while scripted guidance remained competitive in smoothness and predictability. These results provide preliminary evidence that structured actionable knowledge can help ground language-model-based adaptation in embodied assistive interaction, while also highlighting the need for larger, longitudinal studies to evaluate robustness, generalizability, and long-term user experience.
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