Humanoid loco-manipulation demands coordinated body and hand behavior, while conventional robot pre-training data provide limited coverage of such whole-body motion. We present WB-WAM, a World Action Model that incorporates explicit whole-body action supervision into generative video pre-training. A shared physical act...
Chuan Qin, Shao-Ting Zhu, Si-Yuan Luo et al.· 0 citations
Vorch-Omni is presented, a unified multi-task framework for audio-visual synthesis based on an arbitrary-condition-to-arbitrary-output formulation that supports over 10 tasks, including text-to-video, text-to-audio-video, image- and reference-conditioned generation, temporal extension, audio-driven generation, video tr...
Vorch Team, Xiaoyu Chen, Yang Ding et al.· 0 citations
Agentic Real2Sim is introduced, a framework for generalized physical world modeling with vision-language agents, converting a real-world recording of object-robot interaction into a simulatable episodic twin which preserves observations, geometries, robot interactions, and object states.
This work introduces the cross-predictive JEPA (JEPA-x), which grounds latent dynamics in privileged physical trajectories, and shows that direct physical-state regression improves decodability without improving forecastability or control, indicating that the benefit comes from shaping latent dynamics rather than merel...
Kehan Wen, Ziming Li, Si-Yuan Luo et al.· 2 citations
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