LIBERO-MAX evaluates 14 robot policies across 8,000 paired scenarios and 224,000 simulations, showing all policies lose 11–25.7 percentage points in success rate when environments change mid-execution, and even higher observation frequency fails to recover the gap.
The article explains that a high‑accuracy model alone does not guarantee a deployable AI system; it details how inadequate evaluation leads to most production failures and presents a comprehensive, multi‑dimensional evaluation framework—including distributional robustness, fairness, explainability, temporal stability, and efficiency trade‑offs—plus practical CI/CD pipelines and common pitfalls.
The Meituan‑Dianping Technology Salon’s mobile‑testing Q&A covered a robustness‑testing tool that mutates API responses, online recording for functional scripts, deep SDK testing with ADB automation, and server‑side automation frameworks, with detailed discussions on proxy setup, script maintenance, performance metrics, and Docker integration.
