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About this paper
Apurva Badithela, David Snyder, Lihan Zha, Joseph Mikhail, Matthew O'Kelly, Anushri Dixit, Anirudha Majumdar
SureSim introduces a framework that integrates imperfect simulation with limited real-world trials to provide statistically rigorous and cost-effective evaluation of robot policies. It provides finite-sample valid confidence intervals on real-world policy performance, achieving an average reduction of 20-25% in hardware evaluation effort compared to real-only methods.
Key insights
- Prediction-Powered Inference (PPI) effectively combines imperfect simulation with limited real-world data to yield statistically valid and tighter confidence intervals on robot policy performance.
- The effectiveness of simulation augmentation is directly tied to the correlation between real and simulated outcomes; low correlation (large sim-to-real gap) diminishes the inferential benefits.
- Finite-sample valid statistical methods are essential for reliable robot policy evaluation, as methods relying on asymptotic guarantees can exhibit miscoverage in practical, data-limited scenarios.
Summary by alphaXiv.
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