Skip to content

论文arXiv 2608.165902 个月前

Zetta ζ:一种用于自演化物理智能的高效闭环具身框架 ​

原文链接:Zetta $ζ$: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence(arXiv 2608.16590 · 发布日期:2026-08-17)

Zetta $ζ$: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence

Zetta通过三时间尺度闭环在线演化运行时评判器与恢复技能,在LIBERO-Pro和RoboCasa上验证自演化物理智能。

核心要点 ​

  • 现有具身框架多为开环,执行中依赖固定技能,事后反思难以及时治理物理交互。
  • Zetta冻结基础策略,在线演化代码化运行时评判器与恢复技能,实现闭环执行治理。
  • 三类分离时间尺度循环分别负责动作频率治理、rollout级提案和验证门控技能更新。
  • 在LIBERO-Pro和RoboCasa当前rollout预算下达到90.8%和93.6%成功率,并有11.1倍推理加速。

验证范围与局限:验证范围为LIBERO-Pro和RoboCasa当前rollout预算;摘要未说明真机验证或机器人数量。

英文摘要 (Abstract) ​

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 execution: existing harnesses remain largely open-loop, following fixed skills during rollout and reflecting only after an episode completes. Such post-hoc reflection cannot govern execution as it unfolds, because physical interaction requires decisions to track rapidly changing robot-environment states at a frequency beyond today's large agentic models. We present Zetta, a closed-loop embodied harness that evolves code-based runtime critics and recovery skills online while keeping the base policy frozen. Through three timescale-separated loops, Zetta provides action-frequency governance, rollout-level critic-recovery proposal, and validation-gated skill updates. Together with Z-Infra, a rollout infrastructure decoupling agent logic from heterogeneous execution resources, Zetta achieves state-of-the-art success on LIBERO-Pro and RoboCasa under our current rollout budget, reaching 90.8% and 93.6%, with an 11.1x inference speedup; success continues to scale with self-exploration experience; learned skills transfer zero-shot, and clear robotic "Aha Moments" emerge. These results show that closed-loop harness self-evolution opens a scaling path for reliable physical intelligence.

作者:Xin Ding, Liang Mi, Mingzhe Huang, Zixuan Wang, Chao Zhang, Zixu Hao, Fu Chen, Xiangyu Li, Yikai Zheng, Guo, Yaoyu

来源

本页事实对应的公开页面,以原文为准。

  1. arXiv 2608.16590:Zetta $ζ$: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence访问 2026-10-21