SCAPE: Scenario-Conditioned Simulation-Augmented Policy Evaluation
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 evalu...
| Authors | Dijie Zhu, Seunghun Oh, Ruopeng Huang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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