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Humanoid robots struggle to bridge the gap between selecting the right tool and executing complex tasks, with current policies showing significant performance degradation on unseen tools and unrelated instructions.
Reusing an MLLM's causal context as robot memory boosts performance on complex tasks, achieving a remarkable 75.54% accuracy on RoboMME.