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
This work presents SkelWAM, a skeleton-guided world-action model that couples perception and control through one explicit geometric representation for single-source cross-embodiment manipulation, and introduces LIBERO-Cross10, a source-only cross-embodiment transfer benchmark covering ten tasks and ten target embodimen...
Pengbo Niu, Yu-Jia Xie, Rui Peng et al.· 0 citations
CATok is proposed, a causal action tokenizer that reframes tokenization as a causally structured generative process, establishing a high-performance, scalable foundation for purely autoregressive VLA systems.
Chen-Yu Zhang, Yu-Hang Cao, Da-Ru Du et al.· 0 citations
The proposed Representation World Model, which learns states, transitions, and executable plans directly in representation space, suggests that planning directly in representation space provides a promising alternative to conventional world-model planning.
Yi-Jun Yuan, Wei-Cheng Zheng, Wei-Bang Wang et al.· 0 citations
G0.5 is introduced, a pretrained autoregressive VLA in which a single transformer decoder emits reasoning and action tokens under a single objective, which exceeds state-of-the-art models across 7 independent regimes.
Yi-Cheng Liu, Zibin Dong, Baijun Ye et al.· 17 citations· ⚡2
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
