Zetta：用于自进化物理智能的高效闭环具身框架
原标题：Paper page - Zetta ζ: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence
AI 摘要
Zetta是一个闭环具身智能框架，通过在线演化基于代码的运行时批评者和恢复技能，在保持基础策略冻结的同时，以动作频率治理物理执行。在LIBERO-Pro和RoboCasa基准上分别达到90.8%和93.6%的成功率，推理速度提升11.1倍，且技能可零样本迁移，展现出机器人"顿悟时刻"。该研究为可靠物理智能的扩展提供了新路径。
正文节选
Zetta ζ: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence Abstract Zetta is a closed-loop embodied harness that evolves runtime critics and recovery skills online to govern physical execution at action frequency, achieving high success on robot benchmarks with faster inference and scaling self-exploration. Embodied agents are increasingly used to close the gap left by end-to-end policy models. Yet the agentic path has not realized closed-loop learning in physical