1 citations · 2 across the 5 of their papers we have counts for
8 papers · 1 filter
Hi-WM: Human-in-the-World-Model for Scalable Robot Post-Training
Yaxuan Li, Zhongyi Zhou, Yefei Chen +5
Post-training is essential for turning pretrained generalist robot policies into reliable task-specific controllers, but existing human-in-the-loop pipelines remain tied to physica…
Veo-Act: How Far Can Frontier Video Models Advance Generalizable Robot Manipulation?
Zhongru Zhang, Chenghan Yang, Qingzhou Lu +4
Video generation models have advanced rapidly and are beginning to show a strong understanding of physical dynamics. In this paper, we investigate how far an advanced video generat…
VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model
Yanjiang Guo, Tony Lee, Lucy Xiaoyang Shi +3
The goal of this paper is to improve the performance and reliability of vision-language-action (VLA) models through iterative online interaction. Since collecting policy rollouts i…
BagelVLA: Enhancing Long-Horizon Manipulation via Interleaved Vision-Language-Action Generation
Yucheng Hu, Jianke Zhang, Yuanfei Luo +9
Equipping embodied agents with the ability to reason about tasks, foresee physical outcomes, and generate precise actions is essential for general-purpose manipulation. While recen…
Ctrl-World: A Controllable Generative World Model for Robot Manipulation
Yanjiang Guo, Lucy Xiaoyang Shi, Jianyu Chen +1
Generalist robot policies can now perform a wide range of manipulation skills, but evaluating and improving their ability with unfamiliar objects and instructions remains a signifi…
villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models
Xiaoyu Chen, Hangxing Wei, Pushi Zhang +9
Vision-Language-Action (VLA) models have emerged as a popular paradigm for learning robot manipulation policies that can follow language instructions and generalize to novel scenar…