Egocentric human data provides a scalable source of experience for robot learning, but varies substantially in human-robot alignment, behavioral coverage, and available supervision. Existing work shows favorable scaling with increasing human data, but it remains unclear which data properties drive downstream robot gain...
Zhi-Hao Sun, Liu Liu, Xin-Jiang Wang et al.· 0 citations
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.· 1 citation
It is argued that instruction-following evaluation should be text-indispensable: multiple actions should be visually and physically plausible, while only one should be consistent with the language instruction.
Mengao Zhao, Ziang Li, Chaodong Huang et al.· 2 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
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
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