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StretchBot is introduced as a neuro-symbolic framework that integrates multimodal perception and knowledge-graph-grounded LLM reasoning to enable adaptive guidance in assistive robots for stretching routines. This system aims to overcome the limitations of scripted routines by dynamically adjusting to user state, environment, and interaction. A pilot study with three participants suggests that adaptive guidance enhances perceived adaptability and contextual relevance compared to scripted guidance, although the latter remains competitive in smoothness and predictability.
Adaptive robotic stretching is now possible: StretchBot uses LLMs and knowledge graphs to personalize routines based on user state and environment.
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.
