Author

Yu-Hang Zheng

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Preprint Sep 2026

GIFT: Guided Intermediate Feature Training via Action-Oriented Structural Supervision for Robotic Manipulation

Vision-language pre-training and predictive world modeling provide robot policies with rich semantic and dynamic visual features, but their native action and visual-prediction objectives may omit critical physical and task structure while retaining control-irrelevant visual redundancy. We call this mismatch between vis...

Yu-Peng Zheng, Xiang Li, Song-En Gu et al. · 2 citations

Jul 2026

WALA Learning Executable Latent Actions from Action-Labeled Demonstrations and Action-Free Videos

WALA, a framework for learning executable latent actions from both action-labeled demonstrations and action-free videos, achieves strong performance on RoboTwin, sets a new state-of-the-art result on RoboCasa, and improves both policy performance and generalization in real-world manipulation tasks.

Jia-Hao Liu, Zhongpu Xia, Shuai Tian et al. · arXiv.org · 1 citation

Preprint Aug 2026

Latent Action as Intention Enables Efficient Future Imagination for World Action Models

World action models (WAMs) improve robot control by modeling how observations evolve, but generating future observations at test time incurs substantial latency. Fast-WAM removes this process for efficiency; however, our matched implementations show lower generalization for Fast-WAM than for future-aware alternatives,...

Xiang Li, Yu-Peng Zheng, Song-En Gu et al. · 1 citation · ⚡1