MIGU: Multimodal Instruction Grounding under Uncertainty for Manipulation Planning

Abstract:Understanding natural human instructions is crucial for deploying robots in human-centric environments. We study multimodal instruction grounding, where language and gesture provide complementary but uncertain cues. We present MIGU, a modular framework that combines semantic and geometric evidence into a unified grounding belief and connects it to manipulation planning. MIGU constructs a 3D geometric likelihood by propagating viewing-direction and depth uncertainty through eye-finger geometry while accounting for hand-direction estimation error. A vision-language model (VLM) provides semantic priors over candidate objects and regions, which are combined with the geometric likelihood through Bayes-inspired fusion. The resulting belief supports behavior planning to either proceed directly to downstream planning or request clarification. Grounded targets then define goals for mobile manipulation and tabletop task-and-motion planning. On a real-world benchmark, MIGU outperforms all evaluated baselines, while ablations support the benefit of explicit multimodal uncertainty modeling. Project website: this http URL

Robotics (cs.RO)

arXiv:2609.24995 [cs.RO]

(or arXiv:2609.24995v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.24995 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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