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FAN is introduced, which achieves the highest performance and demonstrates consistent robustness, providing insightful guidance for building stable action representations in achieving effective lifelong VLA adaptation.
AtomEgo is presented, a systematic study of ego--robot co-training supported by a curated corpus of approximately 2,659 hours and a scalable data processing pipeline that reveals a simple principle: Data Scale * Alignment Quality -->Capability Gain; egocentric data can improve generalization, but their value depends on...
This work proposes UCA-Flow, a unified condition-action modeling framework for accurate one-step action generation that unifies observation conditions, timestep conditions, interval conditions, and action tokens into a single sequence, and processes them with a Unified Condition-Action Transformer for joint condition-a...
An object-centric 3D representation alignment framework built upon $\pi_0$, using SAM3D as a frozen 3D teacher to provide target-object 3D priors during training, which enables the policy to internalize target-object 3D information while preserving the original RGB-language-to-action inference pipeline without requirin...
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