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Bridging the action-sufficiency gap in robotic manipulation, GIFT achieves up to 12.6 points improvement over existing models by integrating structured intermediate features.
WALA achieves a remarkable 75.2% average success rate on RoboCasa, showcasing the power of combining action-labeled and action-free data for robust robot learning.
Pocket-sized VLA models can now achieve state-of-the-art robot manipulation performance by pre-training on a curated multimodal dataset and injecting manipulation-relevant representations into the action space.
