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HABC achieves up to 92% success in complex robotic tasks by intelligently balancing viability and efficiency in sparse outcome scenarios.
Incorporating egocentric human video data into robotic training can lead to substantial performance gains, with ACE-EGO-0 setting new benchmarks in VLA tasks.
Text world models can transform LLM-based agents from reactive responders into proactive planners, enhancing their performance in complex interactive tasks.
