FUSE: Active Functional Affordance Grounding through Adaptive Semantic-Geometric Evidence Acquisition
This paper introduces Active Functional Affordance Grounding, a task where an embodied agent sequentially explores a scene to identify and spatially ground an object matching a functional query. The authors propose FUSE, an adaptive semantic-geometric evidence acquisition framework combining uncertainty-driven exploration with a learned amortized planner. On a Habitat-based benchmark, FUSE achieves the highest non-oracle grounding performance while reducing computation by 1.33x vs. fully explicit exploration, and remains effective across multiple affordance knowledge sources.
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[Submitted on 13 Aug 2026]
Title:FUSE: Active Functional Affordance Grounding through Adaptive Semantic-Geometric Evidence Acquisition
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Abstract:Embodied agents must often identify and interact with objects based on their function rather than their identity, requiring them to actively acquire observations that reveal discriminative functional evidence. Existing affordance grounding methods operate from fixed viewpoints and lack mechanisms for deciding where to look when functional cues are occluded or incomplete. We introduce Active Functional Affordance Grounding, a new task in which an agent sequentially explores a scene to identify and spatially ground an object satisfying a functional query. To address this problem, we propose FUSE, an adaptive semantic-geometric evidence acquisition framework that combines explicit uncertainty-driven exploration with a learned amortized planner to efficiently select informative viewpoints. We further introduce a Habitat-based benchmark for evaluating active functional grounding. Experiments show that FUSE achieves the highest observed non-oracle grounding performance while reducing computation by 1.33x relative to fully explicit exploration, and remains effective across multiple affordance knowledge sources.
Comments: Under review. 15 Pages. 9 tables, 3 Figures
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.12683 [cs.RO]
(or arXiv:2608.12683v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.12683
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sathyanarayanan Aakur [view email] [v1] Thu, 13 Aug 2026 00:51:09 UTC (2,362 KB)
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