# Zetta zeta: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence

arXiv: https://arxiv.org/abs/2608.16590 · HTML: https://mlsanity.com/paper/2608.16590/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2026.

## Built on by (most cited first, 9 of 9)

- [HarnessPAI: An Evolving Harness for Physical AI](https://mlsanity.com/paper/2609.29166/) (2026)
- [EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution](https://mlsanity.com/paper/2610.10498/) (2026)
- [Self-Evolving Coding Agents: From Digital Programs to Physical-World Intelligence](https://mlsanity.com/paper/2609.35432/) (2026)
- [In-Context Learning for Robots: Methods and Applications](https://mlsanity.com/paper/2609.36012/) (2026)
- [EMHO: EMbodied Agent Harness Optimization via Experience Traces](https://mlsanity.com/paper/2610.08432/) (2026)
- [MaskHarness-WAM: Instance-Grounded Harnessing for Long-Horizon Robot Manipulation](https://mlsanity.com/paper/2609.19974/) (2026)
- [DynaHarness: A Dynamic Physical Harness for Self-Evolving Robot Agents](https://mlsanity.com/paper/2609.40306/) (2026)
- [PhysEvo: Astra Can Act, Let It](https://mlsanity.com/paper/2610.08995/) (2026)
- [RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes](https://mlsanity.com/paper/2610.11480/) (2026)

## Abstract (arXiv)

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.