Egocentric Instruction-oriented Affordance Prediction via Large Multimodal Model

• URL: http://arxiv.org/abs/2508.17922v1
• Date: Mon, 25 Aug 2025 11:40:31 GMT
• Title: Egocentric Instruction-oriented Affordance Prediction via Large Multimodal Model
• Authors: Bokai Ji, Jie Gu, Xiaokang Ma, Chu Tang, Jingmin Chen, Guangxia Li,
• Abstract summary: Affordance is crucial for intelligent robots in the context of object manipulation.<n>In this paper, we argue that affordance should be task-/instruction-dependent.<n>We present a new dataset comprising fifteen thousand object-instruction-affordance triplets.
• Score: 2.393736608344872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Affordance is crucial for intelligent robots in the context of object manipulation. In this paper, we argue that affordance should be task-/instruction-dependent, which is overlooked by many previous works. That is, different instructions can lead to different manipulation regions and directions even for the same object. According to this observation, we present a new dataset comprising fifteen thousand object-instruction-affordance triplets. All scenes in the dataset are from an egocentric viewpoint, designed to approximate the perspective of a human-like robot. Furthermore, we investigate how to enable large multimodal models (LMMs) to serve as affordance predictors by implementing a ``search against verifiers'' pipeline. An LMM is asked to progressively predict affordances, with the output at each step being verified by itself during the iterative process, imitating a reasoning process. Experiments show that our method not only unlocks new instruction-oriented affordance prediction capabilities, but also achieves outstanding performance broadly.

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