SeededGrasp: Language-Guided Grasping in Complex Scenes with Multiple Embodiments

Practical robotic grasping in complex scenes requires both 3D spatial reasoning and alignment with task-specific requirements. Vision-language models (VLMs) offer a natural way to specify these requirements using language, but existing approaches either use a VLM to predict the grasp directly with limited spatial awareness, or train the VLM together with the grasping model, which requires significantly more data and compute. These limitations impede performance and have prevented scaling to mult

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ethics.ai (21 July 2026), “SeededGrasp: Language-Guided Grasping in Complex Scenes with Multiple Embodiments,” evidence record 12656, https://ethics.ai/record/12656 (originally published by HuggingFace Daily Papers).

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