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.
Robodexvlm: Visual language model-enabled task planning and motion control for dexterous robot manipulation
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GTP-FA is a grasp-then-plan framework with failure attribution that diagnoses errors to optimize grasping priors and planning data collection, raising success rates across RL, IL, diffusion, and VLA methods in simulation and real robots.
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.
A structured survey of dexterous robotic hand research that reviews hardware, control methods, data resources, and benchmarks while identifying major limitations and future directions.
citing papers explorer
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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.
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Grasp-Then-Plan with Failure Attribution: A Closed Two-Stage Framework for Precise and Generalizable Robotic Manipulation
GTP-FA is a grasp-then-plan framework with failure attribution that diagnoses errors to optimize grasping priors and planning data collection, raising success rates across RL, IL, diffusion, and VLA methods in simulation and real robots.
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A Survey on Vision-Language-Action Models: An Action Tokenization Perspective
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.
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Towards Robotic Dexterous Hand Intelligence: A Survey
A structured survey of dexterous robotic hand research that reviews hardware, control methods, data resources, and benchmarks while identifying major limitations and future directions.