VLMs generalize affordance inference to non-humanoid robots but produce inconsistent results with a conservative bias of low false positives and high false negatives, especially for novel object manipulations.
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Assessing VLM-Driven Semantic-Affordance Inference for Non-Humanoid Robot Morphologies
VLMs generalize affordance inference to non-humanoid robots but produce inconsistent results with a conservative bias of low false positives and high false negatives, especially for novel object manipulations.