Zetta $ζ$: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence
- Published
- Source
- arXiv
- Paper number
- 927
- Field
- Robotics
- arXiv ID
- 2608.16590
Key points
- LIBERO-Pro uses a fixed pi0.5 policy, and RoboCasa uses a fixed GR00T N1.5 policy; the monitoring code proposes anomalies and the fixed recovery agent decides whether to execute recovery.
- RoboCasa uses 50 development conditions and 50 separate test conditions for each task, and LIBERO-Pro uses 50 development conditions and 20 non-overlapping test conditions.
- RoboCasa rises from 73.56 percent to 93.56 percent over four rounds of cumulative improvement, while Goal T and Goal S in LIBERO-Pro rise from 31.0 percent to 92.5 percent and from 38.0 percent to 89.0 percent, respectively.
- When the recovery skill learned on PnP-Stove is applied to three related tasks without any additional improvement, the average rises from 64 percent to 84 percent, and on eight A100s it processes 16 concurrent runs at 22.09 per minute.
- The abstract's LIBERO-Pro score of 90.8 percent does not match the 71.13 percent overall average in Table 3, so this summary uses the table value; the paper shows real-robot videos but reports no quantitative evaluation there, so the simulation gains should not be read as real-world performance.
Paper links
External research summaries. These are not HDATF publications or measured product results.