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State-of-the-art Vision-Language Models fall short in real-world robotic applications, revealing critical gaps in their reasoning capabilities.
GaP outperforms traditional methods in variational automation tasks by leveraging directed computation graphs for real-time adaptability and improved success rates.
Efficiently navigating multi-floor environments is now possible for autonomous ground vehicles, thanks to a novel optimization-based trajectory planning framework.
Current language agents are still far from matching human expert performance when faced with real-world professional tasks requiring complex reasoning, authoritative source retrieval, and domain-specific knowledge, as revealed by the new \$OneMillion-Bench benchmark.
