GIFT (Guided Intermediate Feature Training), an architecture-flexible framework for learning intermediate features that translates these structures into training-time constraints through geometry alignment, affordance prediction, and goal-region reconstruction, establishes learning functionally structured intermediate...
Yu-Peng Zheng, Xiang Li, Song-En Gu et al.· 2 citations
CauseCollab is proposed, a causal unified and modality-agnostic network that formulates representation learning in the protocol space from a causal perspective, explicitly disentangling semantic factors from modality-specific statistical confounders via causal metric learning.
World action models (WAMs) improve robot control by modeling how observations evolve, but generating future observations at test time incurs substantial latency. Fast-WAM removes this process for efficiency; however, our matched implementations show lower generalization for Fast-WAM than for future-aware alternatives,...
ABot-N1 establishes new state-of-the-art records, delivering massive gains specifically in urban-scale navigation: boosting POI arrival by 35.0% (to 77.3%) and achieving 95.4%/92.9% SR in complex indoor and outdoor scenes.
Ruiyan Gong, Ying-Nan Guo, Junjun Hu et al.· arXiv.org· 4 citations· ⚡1
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