Zetta ζ：一種用於自演化物理智慧的高效閉環具身框架

摘要

具身智能體越來越常被用來填補端到端策略模型所留下的缺口。然而，智能體路徑尚未在物理執行中實現閉環學習：現有的框架在很大程度上仍屬開環，在執行期間遵循固定技能，僅在一個回合結束後進行反思。這種事後反思無法在執行展開的過程中進行調控，因為物理交互要求決策能夠以超越當今大型智能體模型的頻率，追蹤快速變化的機器人-環境狀態。我們提出Zetta，一個閉環具身框架，在保持基礎策略凍結的同時，線上演化基於程式碼的運行時評判器與復原技能。透過三個時間尺度分離的迴路，Zetta提供動作頻率調控、回合級評判-復原提案，以及驗證門控的技能更新。結合Z-Infra（一個將智能體邏輯與異構執行資源解耦的執行基礎設施），Zetta在我們當前的執行預算下於LIBERO-Pro和RoboCasa上達到了當前最優的成功率，分別達到90.8%和93.6%，並實現11.1倍的推理加速；成功率持續隨自我探索經驗而擴展；學習到的技能可零樣本遷移，並湧現出清晰的機器人「頓悟時刻」。這些結果表明，閉環框架的自我演化為可靠物理智能開闢了一條擴展路徑。

English

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.