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SeededGrasp proposes a data-efficient framework that uses a vision-language model to predict a seed point for a lightweight grasp generator, enabling language-guided grasping in complex scenes with multiple robot embodiments. The method outperforms baselines with 72% simulation and 78% real-world success, and includes a new large-scale multi-embodiment grasping dataset.
This paper introduces Agentic RAG-VLM, a unified framework that integrates retrieval-augmented generation with vision-language models and self-reflective planning for generalizable robotic grasping in cluttered environments, achieving 78.3% success rate.
A flow-matching model generates diverse human grasps from RGB-D images, enabling zero-shot robotic grasping with improved performance over existing methods. The model, trained on a large egocentric dataset, significantly outperforms state-of-the-art baselines on a new benchmark.
Researchers explore a data generation pipeline using domain randomization and procedurally generated objects to train a deep neural network for robotic grasp planning. The proposed autoregressive model achieves >90% success on unseen objects in simulation and 80% in the real world, despite being trained only on random simulated objects.