Fan Feng

Causally Debiased Latent Action Model for Embodied Action Conditioned World Models

Yufan Wei, Kun Zhou, Lingjun Mao, Zijun Zhang, Ziming Xu, Ziqiao Xi, et al.

Action-conditioned world models (ACWMs) aim to simulate future observations conditioned on embodied actions, offering a promising foundation for robot planning, policy evaluation, and data augmentation. However, learning controllable ACWMs requires large-scale action-labeled data…Read more

Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling

Fan Feng, Yujia Zheng, Minghao Fu, Yongqiang Chen, Guangyi Chen, Kevin Murphy, et al.

Learning and planning in imagination using world models provides an effective paradigm for training agents for decision-making. However, existing approaches often rely on high-dimensional latent spaces or generic visual embeddings that retain many factors irrelevant to control, l…Read more

Unraveling rate-strengthening and fracture-weakening effects in fractured rock masses: A hybrid bonded particle model-discrete fracture network and physics-inspired machine learning framework

Changtai Zhou, Jiadong Qiu, Murat Karakus, Chengzilong Wang, Fan Feng, Fei Wang, et al.