Agentic RAG-VLM achieves 78.3% success on a 12-task grasping benchmark with 360 trials per configuration, a 53.3 percentage-point gain over VLM-only baselines, via hierarchical affordance RAG, scene graph constraints, and a 14-type failure taxonomy with adaptive retry.
DexVIP: Learning dexterous grasp- ing with human hand pose priors from video,
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Agentic RAG-VLM: Affordance-Aware Retrieval-Augmented Generation with Self-Reflective Planning for Robotic Grasping
Agentic RAG-VLM achieves 78.3% success on a 12-task grasping benchmark with 360 trials per configuration, a 53.3 percentage-point gain over VLM-only baselines, via hierarchical affordance RAG, scene graph constraints, and a 14-type failure taxonomy with adaptive retry.