World action models improve robotic manipulation by conditioning actions on predicted futures, yet existing tactile variants largely inherit video-generation pipelines that reconstruct future tactile observations through iterative denoising. Such prediction can become unreliable under deployment drift: small changes in...
En-Yi Wang, Ming-Xin Wang, Quan Shi et al.· 0 citations
Results show that observable visual-proprioceptive-action history is sufficient to infer latent task states and enable practical failure recovery for existing VLA policies, and the proposed approach substantially improves closed-loop reliability under diverse perturbations.
Chang-Nan Song, Bin Qian, Yan Feng et al.· 0 citations
This work proposes CF-VLA, a coarse-to-fine two-stage formulation that restructures action generation into a coarse initialization step that constructs an action-aware starting point, followed by a single-step local refinement that corrects residual errors.
Fan Du, Feng Yan, Jian-Xiong Wu et al.· arXiv.org· 0 citations
Semantic-Temporal WAM (ST-WAM) is proposed to improve action robustness by using DINOv3 as a shared semantic representation for future prediction and history retrieval while retaining fine-grained VAE dynamics, demonstrating that semantic-temporal modeling effectively complements pixel-generative dynamics for robust ma...
Mingxin Wang, Bin Hu, Bin Qian et al.· arXiv.org· 5 citations· ⚡1
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