Nexus-VLA: Bridging Human and Robot Experiences via a Shared Hierarchical Semantic Latent Space
Overview
This research introduces Nexus-VLA, a hybrid policy learning framework that enables cross-embodiment knowledge transfer through end-to-end human-robot joint training. By establishing a hierarchical semantic alignment paradigm from task intent to fine-grained interactions and designing a shared manipulation latent representation, the method allows human manipulation experience to directly contribute to robot policy learning. Experiments across RoboCasa-GR1, out-of-distribution (OOD) settings, and real-world robot experiments demonstrate an 80.5% task success rate on RoboCasa-GR1, along with advantages in human-data utilization, learning efficiency, and cross-scenario generalization.
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Data origin: human egocentric
“arXiv:2610.42021v1 Announce Type: new Abstract: Large-scale egocentric human videos provide rich and scalable manipulation experience for robot policy learning, yet substantial embodiment gaps in visual appearance, kinematic structure, and action spaces hinder effective knowledge transfer. Existing methods often decouple cross-embodiment knowledge alignment from robot policy learning, making it difficult for human ma”
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- arXiv Robotics — research abstractsSignalNexus-VLA: Bridging Human and Robot Experiences via a Shared Hierarchical Semantic Latent Space
This research introduces Nexus-VLA, a hybrid policy learning framework that enables cross-embodiment knowledge transfer through end-to-end human-robot joint training. By establishing a hierarchical semantic alignment paradigm from task intent to fine-grained interactions and designing a shared manipulation latent representation, the method allows human manipulation experience to directly contribute to robot policy learning. Experiments across RoboCasa-GR1, out-of-distribution (OOD) settings, and real-world robot experiments demonstrate an 80.5% task success rate on RoboCasa-GR1, along with advantages in human-data utilization, learning efficiency, and cross-scenario generalization.
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