SCAPE: Scenario-Conditioned Simulation-Augmented Policy Evaluation

2026-08-19

AI Summary

为解决机器人学习策略在真实世界中的性能评估问题，SCAPE框架通过有限的真实与模拟样本及大规模模拟运行来预测特定场景下的实际性能，并校正模拟偏差。

Abstract

Reliable performance evaluation is a central bottleneck for deploying robot-learning policies in real-world conditions. Real-world testing is faithful but costly and difficult to scale, whereas simulation-based testing scales easily but is inevitably biased by the sim-to-real gap. Existing simulation-augmented methods combine limited real-world rollouts with abundant simulation proxies, but focus on performance averaged over initial conditions and deployment settings. Such population-level averages obscure scenario-specific variation and provide limited guidance about when and where a policy can be safely deployed. We propose SCAPE, a scenario-conditioned simulation-augmented policy evaluation framework that predicts scenario-conditioned real-world policy performance using limited paired sim-and-real samples and large-scale simulation rollouts. SCAPE corrects sim-to-real bias in simulation labels before training the prediction model and calibrates prediction uncertainty through conformal prediction. We validate SCAPE on autonomous driving and quadruped velocity tracking. In sim-to-sim studies, SCAPE reduces scenario-level prediction error by 4.9%/34.7% (driving) and 14.5%/27.7% (quadruped) relative to scene-conditioned neural and aggregate statistical baselines on average. We further evaluate a velocity-tracking policy deployed on a physical Unitree Go2. SCAPE also improves testing sample efficiency, produces narrower calibrated prediction intervals, generalizes better to out-of-distribution scenarios, and enables fine-grained deployment strategies.

Problem

• performance evaluation
• sim-to-real gap
• robot learning
• scenario-conditioned
• simulation-augmented

Innovation

• Scenario-Conditioned
• Simulation-Augmented Policy Evaluation
• Sim-to-Real Bias Correction
• Conformal Prediction