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A compact 0.54M-parameter policy can achieve near-optimal performance on the LIBERO benchmark, challenging the notion that larger models are always better. WHY_IT MATTERS: This research could redefine our understanding of model efficiency in VLA systems, paving the way for more deployment-efficient robotic policies.
FlexLAM achieves superior performance over fixed-capacity models by dynamically adjusting latent action lengths, revolutionizing how we approach action alignment in video decision-making.
YUBI's finger-driven design enables seamless data collection for bimanual manipulation, yielding a dataset that allows for effective policy transfer across diverse robotic systems.
