Noematrix releases RoboRSI, a multi-agent self-evolution framework for robots

Noematrix (穹彻智能) has released RoboRSI, a multi-agent self-evolution framework aimed at real-world robot tasks. It coordinates four agents—Manager, Planner, Engineer, and Reviewer—in an execute-diagnose-revise-re-execute loop, and introduces a top-down skill refinement (TSR) four-layer skill tree to reuse capabilities.

In experiments reported by the company, code solidification lifted the LIBERO benchmark pass rate from 21.5% to 29.0% and cut median token consumption by 29.4%. Zero-shot adaptation improved from 32/120 to 95/120 tasks on LIBERO and from 9/50 to 36/50 on RoboTwin. The release notes also flag that general foundation models such as GPT-6 Astra still show safety and stability problems in real robot control.

Original sources (Chinese)