StretchBot: 支援ロボットによる適応的ガイダンスのためのニューロシンボリックフレームワーク
StretchBot: A Neuro-Symbolic Framework for Adaptive Guidance with Assistive Robots
ストレッチやリハビリを支援するロボットコーチ「StretchBot」を提案。知識グラフと大規模言語モデルを組み合わせ、ユーザーや環境に応じてガイダンスを適応させる。予備実験では適応的ガイダンスが文脈適合性で優れる一方、スクリプト型は滑らかさで競合した。
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著者: Luca Vogelgesang, Ahmed Mehdi Soltani, Mohammadhossein Khojasteh, Xinrui Zu, Stefano De Giorgis, Madalina Croitoru, Filip Ilievski
分類: cs.RO, cs.HC
原文アブストラクト
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.
