A multi-domain affordance benchmark with 273k images and 26k reasoning instructions is introduced, together with a VLM-based grasping pipeline that shows strong zero-shot affordance segmentation and real-robot performance.
Coco- stuff: Thing and stuff classes in context
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RAGNet: Large-scale Reasoning-based Affordance Segmentation Benchmark towards General Grasping
A multi-domain affordance benchmark with 273k images and 26k reasoning instructions is introduced, together with a VLM-based grasping pipeline that shows strong zero-shot affordance segmentation and real-robot performance.