22 papers
DreamWAM: Beyond RGB Future Prediction for World Action Models
Shanglin Yuan, Weiheng Zhao, Xin Shi +6
World Action Models (WAMs) learn action-relevant representations by predicting how the observed world will evolve. Most existing WAMs define this future in RGB space, where task-re…
Faster-WAM: Efficient Inference-Time Future Conditioning for Robust World Action Models
Weiheng Zhao, Haoyi Jiang, Xin Shi +5
World Action Models (WAMs) improve robot manipulation by learning how the environment evolves beyond the current observation. However, existing approaches face a fundamental dilemm…
Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance
Kangsheng Duan, Ziyang Xu, Wenyu Liu +3
While 10B-level industrial foundation models have pushed the boundaries of image inpainting, their prohibitive computational costs severely hinder practical deployment. Constructin…
MotionVLA: Injecting Geometric Motion into Vision-Language-Action Model
Shanglin Yuan, Weiheng Zhao, Xianda Guo +4
Vision-language-action (VLA) models increasingly condition robot policies on history, depth, or 4D features to resolve ambiguity in long-horizon manipulation. However, more spatiot…
Food-R1: A Unified Multi-Task Food Vision-Language Model with Reinforcement Learning
Yu Zhu, Yongkang Li, Wenjie Zhu +5
Recent studies have explored Vision-Language Models (VLMs) for food analysis. However, most existing methods rely primarily on supervised fine-tuning (SFT), which often limits reas…
UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving
Yongkang Li, Lijun Zhou, Sixu Yan +11
Vision-Language-Action (VLA) models have recently emerged in autonomous driving, with the promise of leveraging rich world knowledge to improve the cognitive capabilities of drivin…