HALO-WA is proposed, a hybrid-attention latent-guided online reinforcement learning (RL) framework for WA models, which leverages latent features and action priors from the WA generation process through a lightweight actor-critic adapter to enable fast online adaptation to real deployment errors.
An-Gen Ye, Wei-Jie Ke, Xiao-Feng Wang et al.· arXiv.org· 1 citation
Zero2Skill is presented, a human-robot symbiotic agentic system in which corrections are retained and reused across rounds, and policies fine-tuned on Zero2Skill data match teleoperation-trained policy success at a fraction of collection human cost.
Boyuan Wang, Zhenyuan Zhang, Zhiqin Yang et al.· arXiv.org· 1 citation
GigaWorld-Policy-0.5 preserves the training benefits of future visual dynamics while improving inference efficiency for robot control, and introduces a Mixture-of-Transformers architecture that separates visual dynamics modeling and action generation into specialized experts.
GigaWorld Team, Angen Ye, Ang-Yuan Ma et al.· arXiv.org· 3 citations
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