World-action models jointly learn robot policies and predict future observations, making the representation space an interface between control and prediction. We study the design of this space through controlled comparisons, finding that neither reconstruction fidelity nor pre-trained perceptual features alone ensure e...
Hao-Yi Jiang, Liu Liu, Xin-Jiang Wang et al.· 0 citations
DreamWAM is introduced, which reformulates future prediction as structured world modeling beyond RGB, representing future states through complementary views of appearance, motion, geometry, and semantics, showing that robust world-action learning depends not only on predicting the future, but on representing it in a fo...
Shanglin Yuan, Weiheng Zhao, Xin Shi et al.· 4 citations
TrustVLA is introduced, a mechanism-guided inference-time defense that adapts the Dirichlet evidence framework from trusted classification to monitor per-token, per-layer epistemic uncertainty in VLA policies, providing a retraining-free, mechanism-guided defense for visual-triggered VLA backdoors.
Pin-Han Fu, Xian-Da Guo, Xue-Tao Li et al.· arXiv.org· 0 citations
Faster-WAM introduces a sparse future-conditioning framework that computes future representations once and selectively reuses them throughout action denoising, and proposes SparseMoT to replace ubiquitous layer-wise fusion with selective video-action interaction at a compact subset of network stages, and Interval KV-Fu...
Weiheng Zhao, Haoyi Jiang, Xin Shi et al.· 10 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
