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

TL;DR

StretchBot is a hybrid neuro-symbolic robotic coach that uses multimodal perception and large language models to provide adaptive, context-aware guidance during stretching routines, improving perceived adaptability.

Contribution

The paper introduces StretchBot, a novel neuro-symbolic framework combining perception and knowledge-grounded language reasoning for adaptive assistive robot guidance.

Findings

Adaptive guidance increased perceived relevance and adaptability.

Scripted guidance was rated higher in smoothness and predictability.

Preliminary results suggest structured knowledge aids language-model adaptation.

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 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.