AI Research
Part Grounding, Not Action Knowledge: Locating the Bottleneck in VLM Affordance Prediction
Uncovering why vision-language models fail at precise robotic manipulation tasks.
Benchmarks agree that vision-language models reason poorly about low-level manipulation, but an aggregate accuracy score does not say which step fails. We separate two steps that affordance questions conflate: identifying which part of an object to act on, and knowing what action...
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ArXiv CS.CV
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ArXiv CS.CV
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This research reveals that vision-language models struggle more with pinpointing physical interaction points than with understanding the intent of the actions themselves. By isolating part grounding from action knowledge, developers can now target specific architectural weaknesses to improve robotic manipulation performance.
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