5 papers indexed
Haisheng Su, Zongdai Liu, Xin Jin, Haoxuan Dou, Chengming Hu, Baorun Li, et al.
World Action Models (WAMs) offer a promising paradigm for robotic manipulation by jointly modeling visual state transitions and robot actions. However, existing WAMs are constrained by limited temporal context, coarse episode-level language supervision, and predominantly text-onl…
Shuailei Ma, Jiaqi Liao, Xinyang Wang, Jingjing Wang, Chaoran Feng, Zijing Hu, et al.
Despite the recent promise in robot control, video generative models suffer from a domain mismatch due to their primary focus on content creation. For example, their design inherently prioritizes visual fidelity and creativity over computational efficiency and physical realism. I…
Wei Wu, Fangjing Wang, Fan Lu, He Sun, Shi Liu, Yunnan Wang, et al.
Despite recent progress of VLA foundation models, the disparity between laboratory conditions and real-world applications continues to impede their practical implementation. To bridge this gap, we present LingBot-VLA 2.0, which advances LingBot-VLA through improvements in three f…
Jianjie Fang, Yongyan Xu, Ziyou Wang, Chen Gao, Yuchao Huang, Zhaolu Wang, et al.
World models are rapidly becoming a core infrastructure for embodied intelligence and interactive agents: they provide controllable simulators in which agents can perceive, act, forecast, and acquire scalable experience. Yet current video generation world models are still organiz…
Xiaomeng Fan, Wei Wu, Yuwei Wu, Zhi Gao, Shiyu Luo, Mingyang Gao, et al.
Multimodal large language models (MLLMs) are increasingly expected to generate fine-grained descriptions of visual content. However, we observe and theoretically show that generating fine-grained responses poses a reliability challenge, \textit{i.e.}, fine-grained generation is m…