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This paper presents RoboDawn, a method to transfer Vision-Language Model intelligence to robotic control, achieving state-of-the-art results on benchmarks with zero-shot and one-shot learning and successful real-world applications.
PolicyMem proposes a geometric policy memory for LLM governance, externalizing natural-language policies as reusable geometric objects to enable detect-rewrite-verify loops with state-of-the-art unsafe behavior detection.
Zetta introduces a closed-loop embodied harness that evolves runtime critics and recovery skills to govern physical execution in real-time, achieving state-of-the-art success on robotics benchmarks with significant inference speedup and self-evolution.