World-Action Models inherit world knowledge from video-generative priors, and channel it into executable control signals through embodied experience. Existing systems, however, are monolithic: the generative backbone, visual representation, architecture, information flow, inference procedure, and training data are tigh...
Yu-Ran Wang, Siqiao Huang, Ming-Lei Li et al.· 38 citations· ⚡4
Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tiny objects, and it has to be fast enough for closed-loop control. Yet vision-language-action (VLA) and world-action models (WAMs) take perception from general-purpose vision-language and video-generati...
RMBench is introduced, a simulation benchmark comprising 9 manipulation tasks that span multiple levels of memory complexity, enabling systematic evaluation of policy memory capabilities and Mem-0, a modular manipulation policy with explicit memory components designed to support controlled ablation studies.
Tian-Xing Chen, Yu-Ran Wang, Ming-Lei Li et al.· arXiv.org· 62 citations· ⚡23
Results show that making touch available does not by itself ensure effective multimodal fusion, and SoftVTBench provides a common visuo-tactile resource for studying not only whether a policy succeeds, but how it physically interacts with deformable objects and when touch improves that interaction.
Bowen Jing, Ming-Xin Wang, Ruiyang Hao et al.· 2 citations
Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tiny objects, and it has to be fast enough for closed-loop control. Yet vision-language-action (VLA) and world-action models (WAMs) take perception from general-purpose vision-language and video-generati...
Qi-Ze Yu, Lian-Rui Fan, Bo-Yu Chen et al.· 0 citations
SCALE (Selective Control of Adaptation via Local Entropy), an entropy-guided adaptation-strength-control method that freezes the pretrained model and the SFT delta and learns bounded token- and module-specific gates by minimizing predictive entropy alone is proposed.
Cun-Chun Li, Hao-Nan He, Yi-Fan Gao et al.· 0 citations
UMI-Bridge, which uses UMI as an intermediate domain to align representations according to action equivalence rather than pixel similarity, supports action-anchored latent alignment for data-efficient robot learning and UMI-to-robot task transfer.
Hai-Yi Liu, Jin-Ming Ma, Ke Rui et al.· 0 citations
Action-conditioned objectives improve the geometry used by Euclidean-cost, CEM-based latent MPC, and DA-LeWM augments LeWM with inverse-dynamics and demonstration-conditioned goal-action heads, and accelerates convergence and achieves higher online success than LeWM.
Jia-Wei Wang, Ke Rui, Yu-Shen Zuo et al.· 8 citations
HeteroGenManip is proposed, a task-conditioned, two-stage framework designed to decouple initial grasp from complex interaction execution, and achieves robust intra-category shape and pose generalization.
GaussianWAM is proposed, a training-time representation-enhancement framework that organizes geometric and semantic supervision through a 3D Gaussian field and improves performance on standard LIBERO and shows positive transfer trends on RoboTwin and real-world manipulation.
Zi-Jian Zhang, Yu-Qing Jiang, Wei-Tao Zhou et al.· 3 citations
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