Bridged SBI: Correcting Biased Low-Fidelity Posteriors for Cost-Efficient High-Fidelity Inference
2026-06-08 • Robotics
Robotics
AI summaryⓘ
The authors address the problem of tuning particle-based simulators used for robotic earthwork, which is difficult because these simulators are complex and slow to run at high accuracy. They propose a method called Bridged SBI that first uses cheaper, less detailed simulations to find a rough estimate of parameters, then corrects these guesses to match the accurate simulator better. This approach helps avoid errors that happen if low-detail simulations are trusted too much without adjustment. Their tests show Bridged SBI gives better and more reliable parameter estimates with less computational effort than using only high-accuracy simulations or simpler multi-fidelity methods.
particle-based simulatorsrobotic earthworksimulation-based inference (SBI)high-fidelity simulationlow-fidelity simulationposterior distributionparameter calibrationmulti-fidelity modelingresidual correctionsim-to-sim calibration
Authors
Gahee Kim, Yuki Kadokawa, Sandro M. Alcantara Tacora, Taro Abe, Daisuke Endo, Genki Yamauchi, Takeshi Hashimoto, Takamitsu Matsubara
Abstract
Accurate calibration of particle-based simulators is crucial for robotic earthwork simulation, but analytical calibration is challenging due to this task's highly nonlinear particle dynamics and the black-box nature of conventional simulators. Although simulation-based inference (SBI) can estimate posterior distributions over simulation parameters solely from forward simulations, applying SBI directly to high-fidelity (HF) particle simulators is often computationally prohibitive. Low-fidelity (LF) simulators with coarser particles can reduce this cost, but changes in particle size and particle count shift the parameter values needed to reproduce the same observation, producing biased LF posteriors. We propose Bridged SBI, which leverages a biased but informative LF posterior to guide HF inference. This method first uses inexpensive LF simulations to identify a coarse high-density parameter region, and then it learns a local residual bridge to transport LF posterior samples toward HF-consistent regions by correcting the LF--HF discrepancy. We analyze how sequential multi-fidelity SBI (Naive-MF) can suffer from LF-induced posterior miscoverage when it directly relies on the LF posterior without discrepancy correction. We then show that Bridged SBI is designed to alleviate this issue by explicitly modeling the LF--HF discrepancy through residual correction. Experiments on both sim-to-sim particle-parameter calibration and real-to-sim calibration with real soil observation show that Bridged SBI produces more accurate and reliable HF posteriors than HF-only SBI or the Naive-MF baseline, especially under limited HF simulation costs.