Author

F. Jiang

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Preprint Oct 2026

Grounded-Dreamer: Robot Learning from World Model Synthetic Data with Physical Grounding

Scaling robot manipulation policies requires large amounts of diverse, action-labeled data, yet collecting such data in the real world remains expensive and difficult to scale. Existing methods either depend on human teleoperation, rely on simulation with limited visual realism, or use human videos that lack embodiment...

F. Jiang, Shu Chen, Yuan-Fei Wang et al. · 0 citations

Preprint Aug 2026

LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models

LD4WAM is presented, which pairs a Latent Dynamics Model trained with semantic reconstruction and real motion alignment with a World Dynamics Action Model built as a mixture-of-transformers (MoT), which preserves full future-video generation and uses learnable queries to distill these latent dynamics from generated fut...

Zhen Shen, Jia-Qi Liang, Jasper Lu et al. · 3 citations