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Within this trained-task regime, DEM sits on the observed success--latency--energy frontier and provides a strong, efficient baseline for language-conditioned robot skills.
CMT-UNet, a CNN-Mamba-Transformer hybrid architecture built on an architecture-level principle for efficient global–local coupling in dense prediction, achieves favorable accuracy–efficiency trade-offs against mainstream CNN-, Transformer-, and Mamba-based alternatives.
Artificial Foveated Perception is proposed, a lightweight, policy-agnostic module that takes the same vision and language inputs as Vision-Language-Action and World Action Model pipelines and predicts task-conditioned masks over relevant objects, the robot, and other action-critical regions and reduces fine-tuning time...
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