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Yuan-Fei Wang

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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
#artificial intelligence Preprint Sep 2026

HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface

Large-scale vision-language-action (VLA) models provide powerful priors for robot manipulation, yet adapting them to a specific deployment remains challenging. Supervised fine-tuning (SFT) on task-specific demonstrations provides a step toward deployment, but faces two persistent limitations: static data provide limite...

Zimu Han, Yi-Ming Zeng, Ji-Yao Zhang et al. · 0 citations

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