World action models (WAMs) aim to answer a coupled physical question: given a task instruction, what motion should the robot execute, and how will that motion change the surrounding world? Most existing WAMs build on pretrained video generators and represent world evolution through images or visual latents. Robotic int...
Rui-Xiang Wang, Yong-Yi Su, Wen-Lve Zhou et al.· 0 citations
Mobile humanoid manipulation requires both reaching a usable workspace and preserving precise hand-object interactions as object poses and contact conditions change. Learning these behaviors from limited task-specific data remains challenging. To bridge this gap, we introduce Praxis, a whole-body manipulation framework...
Shu-Liang He, Rui-Yang Xu, Bo Yue et al.· 0 citations
This paper proposes TaskAnchor, a lightweight adapter that grounds task state by injecting execution context into the VLA's native input space, and real-robot experiments further validate reliable multi-stage execution.
Heng-Yan Liu, Wen-Lve Zhou, Bo Yue et al.· 0 citations
Reactive vision--language--action (VLA) models struggle with long-horizon manipulation when visually similar observations can correspond to different actions depending on the task stage or interaction history. We refer to this ambiguity as task-state aliasing and introduce TaskAnchor, a lightweight adapter that grounds...
Heng-Yan Liu, Wen-Lve Zhou, Bo Yue et al.· 0 citations
This work presents RoboReact, a framework that automatically synthesizes whole-body humanoid manipulation skills from a single egocentric RGB-D observation, and highlights the potential of combining generative models, vision-language reasoning, and closed-loop control for scalable humanoid skill acquisition.
Shu-Liang He, Shuai Wang, Bo Yue et al.· 3 citations· ⚡1
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
