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.

翻译：辅助机器人在家庭和医疗保健环境中支持身体健康的潜力日益增长，例如通过引导用户进行拉伸或康复训练。然而，现有系统大多仍为脚本化运行，这限制了其适应用户状态、环境情境和交互动态的能力。在本工作中，我们提出了StretchBot——一种面向自适应辅助引导的混合神经符号机器人教练。该系统结合多模态感知与基于知识图谱的大语言模型推理，以支持在短时拉伸训练期间进行情境感知调整，同时保持结构化流程。为补充系统描述，我们报告了一项探索性先导研究，比较了脚本化引导与自适应引导在三位参与者中的效果。先导研究发现，自适应条件在感知适应性和情境相关性方面有所提升，而脚本化引导在流畅性和可预测性方面仍具竞争力。这些结果初步表明，结构化的可操作知识有助于在具身辅助交互中为基础语言模型的自适应提供依据，同时也凸显了开展更大规模、纵向研究以评估其鲁棒性、泛化能力及长期用户体验的必要性。