StretchBot: A Neuro-Symbolic Framework for Adaptive Guidance with Assistive Robots

Abstract 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 observations indicate that the adaptive condition was rated higher for perceived adaptability and contextual relevance, while scripted guidance remained competitive in smoothness and predictability. These observations suggest that structured actionable knowledge may help ground language-model-based adaptation in embodied assistive interaction, while highlighting the need for larger, longitudinal studies to evaluate robustness, generalizability, and long-term user experience.