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NeuroVLA is introduced, a brain-inspired architecture that couples a cortical vision-language planner to recurrent cerebellar modulation and a stateful spiking spinal controller that translates semantic intent into actions while incorporating high-frequency proprioceptive and force feedback, maintaining short-term moto...
Adapting general-purpose large language models to specific tasks requires substantial human effort in designing data and training strategies. Sustaining improvement is especially challenging because model updates change the error distribution, requiring strategies to be continually refined. We introduce ImproveAnyTask,...
This paper designs TACT to revise student-generated actions, test the revisions through partner responses, and distill useful feedback into the student, which helps improve social goal attainment while reducing communication cost.
This work provides a geometric interpretation of FM uncertainty in the velocity field, showing that uncertainty manifests as deviation from an ideal affine-isotropic contraction field, and introduces denoising acceleration ( accel), a highly-generalizable and cost-free uncertainty proxy that measures the bending of the...
Flow-matching policies are promising for imitation learning because they model complex multimodal action distributions. However, their stochasticity is largely passive: repeated sampling may yield diverse behaviors, but users cannot directly choose among valid continuations from the same state. We propose Source-Lifted...
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