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ADAPT enables humanoid robots to seamlessly execute diverse motion skills in real-time based on text commands, revolutionizing interactive control.
Surprisingly, larger LLMs benefit from increased repetition of high-quality domain data, challenging conventional wisdom about data diversity in training.
MANGO-Grasp achieves a remarkable 16.57 percentage point boost in zero-shot grasping performance across diverse robotic hands by leveraging geometry-oriented 3D Gaussian representations.
A 99% reduction in token count without sacrificing spatial and semantic integrity leads to a 22.8% boost in real-world robotic task success rates.
