Betting for Sim-to-Real Performance Evaluation

TL;DR

This paper introduces a betting-based framework for more accurate and efficient robot performance evaluation, especially under limited real-world testing constraints, by leveraging theoretical insights and practical algorithms.

Contribution

It develops a novel betting mechanism approach for sim-to-real performance estimation, outperforming traditional Monte Carlo methods with theoretical guarantees and practical decision rules.

Findings

Betting strategies can outperform Monte Carlo estimators in sim-to-real evaluation.

Theoretical conditions ensure the accuracy and efficiency of the proposed betting methods.

Empirical results demonstrate the effectiveness of the approach on synthetic and real robotic tasks.

Abstract

This paper studies the problem of robot performance evaluation, focusing on how to obtain accurate and efficient estimates of real-world behavior under severe constraints on physical experimentation. Such estimates are essential for benchmarking algorithms, comparing design alternatives, validating controllers, and supporting certification or regulatory decision-making, yet real-world testing with physical robots is often expensive, time-consuming, and safety-limited. To mitigate the scarcity of real-world trials, sim-to-real methodologies are commonly employed, using low-cost simulators to inform, supplement, or prioritize physical experiments. Departing from (and complementary to) existing approaches in variance reduction (e.g., importance-sampling variants) or bias-correction (e.g., through prediction-powered inference or learned control variates), we examine this performance-evaluation problem through the lens of betting. We establish theoretical conditions under which a betting mechanism can yield accurate and efficient estimates (provably outperforming the Monte Carlo estimator) and we characterize how such bets should be constructed. We further develop theoretically grounded yet practically implementable approximations of the ideal bet, and we provide concrete decision rules that diagnose when these approximate betting strategies are working as intended. We demonstrate the effectiveness of the proposed methods using both synthetic examples and cross-fidelity computational simulators. Notably, we also showcase an illustrative case in which a group of synthetic distributions are used to infer the real-world pick-and-place accuracy of a robotic manipulator, a seemingly unconventional sim-to-real transfer that becomes natural and feasible under the proposed betting perspective. Programs for reproducing empirical results are available at https://github.com/ISUSAIL/Bet4Sim2Real.