SCAPE: Scenario-Conditioned Simulation-Augmented Policy Evaluation

• URL: http://arxiv.org/abs/2608.19425v1
• Date: Wed, 19 Aug 2026 20:24:29 GMT
• Title: SCAPE: Scenario-Conditioned Simulation-Augmented Policy Evaluation
• Abstract summary: SCAPE is a scenario-conditioned simulation-augmented policy evaluation framework.<n>It predicts scenario-conditioned real-world policy performance using limited paired sim-and-real samples and large-scale simulation rollouts.<n>We validate SCAPE on autonomous driving and quadruped velocity tracking.
• Score: 18.546902084606632
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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