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RealDex: Towards Human-like Grasping for Robotic Dexterous Hand
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In this paper, we introduce RealDex, a pioneering dataset capturing authentic dexterous hand grasping motions infused with human behavioral patterns, enriched by multi-view and multimodal visual data. Utilizing a teleoperation system, we seamlessly synchronize human-robot hand poses in real time. This collection of human-like motions is crucial for training dexterous hands to mimic human movements more naturally and precisely. RealDex holds immense promise in advancing humanoid robot for automated perception, cognition, and manipulation in real-world scenarios. Moreover, we introduce a cutting-edge dexterous grasping motion generation framework, which aligns with human experience and enhances real-world applicability through effectively utilizing Multimodal Large Language Models. Extensive experiments have demonstrated the superior performance of our method on RealDex and other open datasets. The complete dataset and code will be made available upon the publication of this work.
Forward citations
Cited by 6 Pith papers
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MAD-HOI: Masked Autoregressive Diffusion for Generating Articulated Hand Object Interactions from Text
Masked autoregressive diffusion over disentangled continuous latent streams generates, completes, infills, and terminates hand-object interactions from text on ARCTIC and GRAB.
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HRDexDB: A Paired Human-Robot Dataset for Cross-Embodiment Dexterous Grasping
HRDexDB provides 2.1K paired markerless human and multi-robot dexterous grasp sequences on 100 objects with multi-view 3D ground truth and tactile signals.
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Dexonomy: Synthesizing All Dexterous Grasp Types in a Grasp Taxonomy
A two-stage optimization pipeline produces 9.5 million validated grasps across 31 GRASP taxonomy types and enables a type-conditional generative model to grasp novel objects from single-view point clouds with 82.3% re...
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BODex: Scalable and Efficient Robotic Dexterous Grasp Synthesis Using Bilevel Optimization
A GPU-parallel bilevel optimization pipeline synthesizes high-quality dexterous grasps faster than prior methods and produces a dataset that improves learned grasping performance from about 40% to 80% in simulation.
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Bimanual Grasp Synthesis for Dexterous Robot Hands
BimanGrasp produces a large-scale simulated dataset of bimanual dexterous grasps and a diffusion model that synthesizes them at quasi-real-time speeds.
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FastGrasp: Efficient Grasp Synthesis with Diffusion
A one-stage latent diffusion model with an adaptation module generates MANO hand grasping poses from object point clouds faster and with lower penetration than two-stage optimization baselines.
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