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This work proposes a normalization-only attribution control: compare the encoder against its normalization before interpreting transfer as learned invariance, and confirms invariance develops only when variation reaches the encoder past normalization.
The agreement between offline orientation gains and reduced real-robot contact-pose failures supports causal future-token distillation as an effective way to improve dynamic manipulation without deploying the world-model teacher.
The FWBC-VLA is proposed, a force-aware framework that bridges task-level VLA action generation and low-level whole-body compensation control for wheeled-legged robots and demonstrates the effectiveness of the FWBC-VLA in contact-rich loco-manipulation.
DECOWAM is introduced, a whole-body world-action model that separates camera ego-motion from base and arm actions through dedicated conditional interfaces and shows that embodiment-aware factorization can support parameter-efficient joint visual prediction and whole-body control under moving viewpoints.
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