3 papers
cs.RO2026
Data-Free Continual Learning for World Action Model with Rectified Generative Replay
Kaipeng Fang, Hangxu Liu, Chaojian Shi +5
World action models (WAMs) built on pretrained video generation models adapt quickly to new robotic tasks, but they forget previously learned tasks under sequential fine-tuning. Ex…
cs.RO2026
WSA: a 3D-Centric World-Spatial-Action Model for Generalizable Robot Control
Jiahao Jiang, Jianing Zhang, Zhenhan Yin +9
World-Action Models (WAMs) have emerged as a promising paradigm for robot control by using predicted world evolution to guide action generation. However, existing WAMs largely foll…
cs.RO2026
Language-Grounded Decoupled Action Representation for Robotic Manipulation
Wuding Weng, Tongshu Wu, Liucheng Chen +5
The heterogeneity between high-level vision-language understanding and low-level action control remains a fundamental challenge in robotic manipulation. Although recent methods hav…