AffordJEV: Affordance Grounding by Confirming Candidate Regions
AffordJEV: Affordance Grounding by Confirming Candidate Regions
This research presents AffordJEV, a novel approach for affordance grounding in embodied manipulation. The method employs a System One model that directly evaluates candidate regions proposed by frozen segmenters without intermediate text generation, enabling efficient inference and direct semantic control over region extents. The framework utilizes a hierarchical confirmation process from object to part levels, with bidirectional evidence passing between levels to correct decisions. Trained on the InstructPart dataset, AffordJEV outperforms existing chain-of-thought methods in domain-specific tasks and achieves better zero-shot transfer performance on UMD and IIT-AFF benchmarks than comparable System One models. The authors claim this is the first approach to apply System One models specifically to affordance grounding tasks.
The paper introduces AffordJEV, a System One model framework that directly selects candidate affordance regions without generating text, demonstrating improved zero-shot transfer to UMD and IIT-AFF benchmarks compared to chain-of-thought methods.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Source：arXiv Robotics — research abstracts · arxiv.org