Betting for Sim-to-Real Performance Evaluation

• URL: http://arxiv.org/abs/2604.24018v1
• Date: Mon, 27 Apr 2026 03:58:50 GMT
• Title: Betting for Sim-to-Real Performance Evaluation
• Abstract summary: We develop theoretical conditions under which a betting mechanism can yield accurate and efficient estimates.<n>We provide concrete decision rules that diagnose when these approximate betting strategies are working as intended.<n>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.
• Score: 5.669264620577287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Recovering Direct Price Effects of Environmental Amenities in Housing Markets: Regression and Causal Machine Learning Model Assessment with Empirical Monte Carlo Simulation [0.0]
  Hedonic price models are widely used to assess how environmental amenities affect property values.<n>We conduct an empirical Monte Carlo simulation to evaluate the performance of traditional and causal machine learning approaches.
  arXiv  Detail & Related papers  (2026-06-01T18:59:44Z)
• PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies [88.78188489161028]
  We introduce Policy Evaluation and Environment Reconstruction in Simulation (PolaRiS)<n>PolaRiS is a scalable real-to-sim framework for high-fidelity simulated robot evaluation.<n>We show that PolaRiS evaluations provide a much stronger correlation to real world generalist policy performance than existing simulated benchmarks.
  arXiv  Detail & Related papers  (2025-12-18T18:49:41Z)
• Reliable and Scalable Robot Policy Evaluation with Imperfect Simulators [9.868826622074899]
  SureSim is a framework to augment large-scale simulation with relatively small-scale real-world testing.<n>We leverage non-asymptotic mean estimation algorithms to provide confidence intervals on mean policy performance.<n>Our approach saves over (20-25%) of hardware evaluation effort to achieve similar bounds on policy performance.
  arXiv  Detail & Related papers  (2025-10-05T20:37:53Z)
• Uncertainty-Guided Expert-AI Collaboration for Efficient Soil Horizon Annotation [0.13999481573773068]
  We apply conformal prediction to $textitSoilNet$, a multimodal multitask model for describing soil profiles.<n>We design a simulated human-in-the-loop (HIL) annotation pipeline, where a limited budget for obtaining ground truth annotations is available when model uncertainty is high.<n>Experiments show that conformalizing SoilNet leads to more efficient annotation in regression tasks and comparable performance scores in classification tasks.
  arXiv  Detail & Related papers  (2025-09-29T14:54:23Z)
• Prediction-Powered Causal Inferences [59.98498488132307]
  We focus on Prediction-Powered Causal Inferences (PPCI)<n>We first show that conditional calibration guarantees valid PPCI at population level.<n>We then introduce a sufficient representation constraint transferring validity across experiments.
  arXiv  Detail & Related papers  (2025-02-10T10:52:17Z)
• Testing Generalizability in Causal Inference [3.547529079746247]
  No formal procedure exists for statistically evaluating generalizability in machine learning algorithms.<n>We propose a systematic framework for statistically evaluating the generalizability of high-dimensional causal inference models.
  arXiv  Detail & Related papers  (2024-11-05T11:44:00Z)
• AExGym: Benchmarks and Environments for Adaptive Experimentation [7.948144726705323]
  We present a benchmark for adaptive experimentation based on real-world datasets. We highlight prominent practical challenges to operationalizing adaptivity: non-stationarity, batched/delayed feedback, multiple outcomes and objectives, and external validity.
  arXiv  Detail & Related papers  (2024-08-08T15:32:12Z)
• Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale [53.152460508207184]
  Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to a new domain without access to target domain labels or source domain data. This paper proposes a novel approach that considers multiple prediction hypotheses for each sample and investigates the rationale behind each hypothesis. To achieve the optimal performance, we propose a three-step adaptation process: model pre-adaptation, hypothesis consolidation, and semi-supervised learning.
  arXiv  Detail & Related papers  (2024-02-02T05:53:22Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)
• Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation [54.72195809248172]
  We present a new estimator leveraging our proposed novel concept, that involves retrospective reshuffling of participants across experimental arms at the end of an RCT. We prove theoretically that such an estimator is more accurate than common estimators based on sample means.
  arXiv  Detail & Related papers  (2023-02-06T05:17:22Z)
• Validation of Composite Systems by Discrepancy Propagation [4.588222946914529]
  We present a validation method that propagates bounds on distributional discrepancy measures through a composite system. We demonstrate that our propagation method yields valid and useful bounds for composite systems exhibiting a variety of realistic effects.
  arXiv  Detail & Related papers  (2022-10-21T15:51:54Z)
• A User's Guide to Calibrating Robotics Simulators [54.85241102329546]
  This paper proposes a set of benchmarks and a framework for the study of various algorithms aimed to transfer models and policies learnt in simulation to the real world. We conduct experiments on a wide range of well known simulated environments to characterize and offer insights into the performance of different algorithms. Our analysis can be useful for practitioners working in this area and can help make informed choices about the behavior and main properties of sim-to-real algorithms.
  arXiv  Detail & Related papers  (2020-11-17T22:24:26Z)
• DISCO: Double Likelihood-free Inference Stochastic Control [29.84276469617019]
  We propose to leverage the power of modern simulators and recent techniques in Bayesian statistics for likelihood-free inference. The posterior distribution over simulation parameters is propagated through a potentially non-analytical model of the system. Experiments show that the controller proposed attained superior performance and robustness on classical control and robotics tasks.
  arXiv  Detail & Related papers  (2020-02-18T05:29:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.