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
LIRA is introduced, a local cross-layer action-conditioning mechanism that formulates VLM-to-action conditioning as depth-aware information routing and improves the principal aggregate metrics over the VLA-Adapter baseline under the same 0.5B-parameter configuration.
Zhe-Wei Zhang, Pu Wang, Guan-Ren Qiao et al.· 4 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
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
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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