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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.
RLMF not only boosts LLMs' ability to accurately express uncertainty but also enhances their self-assessment capabilities, fundamentally reshaping their trustworthiness.
Autoregressive policies can achieve real-time execution with superior performance and speed, challenging the dominance of diffusion-based approaches.
