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
Accurate simulation of rigid-body interactions is essential for predictive physical world models. Despite recent progress in modeling object dynamics, capturing how local contacts between surfaces shape object motion remains challenging. While end-to-end world models predict interactions across entire scenes or objects...
Rui Ouyang, Guan-Ren Qiao, Fan-Sen Meng et al.· 0 citations
Recent vision-based action models have demonstrated strong capabilities in complex manipulation, but they rarely leverage explicit object physical properties to adapt their policies. We introduce ViTacPhys, a visual-tactile framework and data acquisition system that estimates object mass and friction-coefficient classe...
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
The RoboSynChallenge competition introduces a unified benchmark to evaluate and advance the generalizability of manipulation policies across a spectrum of tasks, environments, and difficulty levels and aims to foster the development of broadly capable, data-efficient, and adaptable manipulation systems, thereby paving...
Runyi Zhao, Rui-Han Wu, Cheng-Kun Li et al.· 0 citations
Vid2WAM is proposed, an offline distillation framework that transfers visual diffusion priors from a large video foundation model into a compact WAM student and introduces source-aware residual action adaptation that learns source-specific corrections around a shared action backbone and mitigates interference from nois...
Chen-Hao Qiu, Ruixiang Wang, Runyi Zhao et al.· 2 citations
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