
ロボットが人間の自然言語による指示に基づいて器用な把持を実行できるようにするため、大規模データセットDexGYSNetと、学習の難しさを軽減する段階的学習戦略を採用したDexGYSGraspフレームワークを提案する。


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人間が言葉で指示した通りに器用につかむ-言語ガイド付き器用な把持生成


人間が言葉で指示した通りに器用につかむ：言語ガイド付き器用な把持生成


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This paper introduces a novel framework, DexGYSGrasp, that enables robots to generate diverse and high-quality dexterous grasps from natural language instructions, addressing the limitations of previous methods that struggle with intention alignment, diversity, and object penetration.


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Generating Human-like Dexterous Grasps from Natural Language Instructions



The proposed Dexterous Grasp Transformer (DGTR) framework can efficiently predict a diverse set of feasible dexterous grasp poses by processing the input object point cloud in a single forward pass.


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Dexterous Grasp Transformer: Predicting Diverse and High-Quality Robotic Grasps from Object Point Clouds
