RACaP is introduced, an agentic framework that moves coding to evolution and uses a Reasoning-and-Acting loop to call frozen, typed Policy APIs at deployment and shows that separating reusable code from runtime decisions supports continued evolution, effective transfer, and efficient long-horizon control.
Ze-Xi Li, Ye-Hang Zhang, Hao-Jian Huang et al.· 1 citation
A literature review around the interfaces connecting contextual evidence to execution, distinguishing four families: context-conditioned policies, geometric demonstration transfer, world-model-based control, and skill- and agent-based execution.
Hao-Jian Huang, Ze-Xi Li, Ju-Hao Guo et al.· 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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