ASBI: Leveraging Informative Real-World Data for Active Black-Box Simulator Tuning

• URL: http://arxiv.org/abs/2510.15331v1
• Date: Fri, 17 Oct 2025 05:38:33 GMT
• Title: ASBI: Leveraging Informative Real-World Data for Active Black-Box Simulator Tuning
• Authors: Gahee Kim, Takamitsu Matsubara,
• Abstract summary: Black-box simulators are widely used in robotics, but optimizing their parameters remains challenging due to inaccessible likelihoods.<n>We present Active Simulation-Based Inference (ASBI), a parameter estimation framework that uses robots to actively collect real-world online data.
• Score: 4.246528354565986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Black-box simulators are widely used in robotics, but optimizing their parameters remains challenging due to inaccessible likelihoods. Simulation-Based Inference (SBI) tackles this issue using simulation-driven approaches, estimating the posterior from offline real observations and forward simulations. However, in black-box scenarios, preparing observations that contain sufficient information for parameter estimation is difficult due to the unknown relationship between parameters and observations. In this work, we present Active Simulation-Based Inference (ASBI), a parameter estimation framework that uses robots to actively collect real-world online data to achieve accurate black-box simulator tuning. Our framework optimizes robot actions to collect informative observations by maximizing information gain, which is defined as the expected reduction in Shannon entropy between the posterior and the prior. While calculating information gain requires the likelihood, which is inaccessible in black-box simulators, our method solves this problem by leveraging Neural Posterior Estimation (NPE), which leverages a neural network to learn the posterior estimator. Three simulation experiments quantitatively verify that our method achieves accurate parameter estimation, with posteriors sharply concentrated around the true parameters. Moreover, we show a practical application using a real robot to estimate the simulation parameters of cubic particles corresponding to two real objects, beads and gravel, with a bucket pouring action.

Related papers

• Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference [12.019504660711231]
  We introduce sequential neural posterior estimation (ASNPE)<n>ASNPE brings an active learning scheme into the inference loop to estimate the utility of simulation parameter candidates to the underlying probabilistic model.<n>Our method outperforms well-tuned benchmarks and state-of-the-art posterior estimation methods on a large-scale real-world traffic network.
  arXiv  Detail & Related papers  (2024-12-07T08:57:26Z)
• Compositional simulation-based inference for time series [21.9975782468709]
  Methods train neural networks on simulated data to perform Bayesian inference.<n> simulators emulate real-world dynamics through thousands of single-state transitions over time.<n>We propose an SBI approach that can exploit such Markovian simulators by locally identifying parameters consistent with individual state transitions.
  arXiv  Detail & Related papers  (2024-11-05T01:55:07Z)
• Embed and Emulate: Contrastive representations for simulation-based inference [11.543221890134399]
  This paper introduces Embed and Emulate (E&E), a new simulation-based inference ( SBI) method based on contrastive learning. E&E learns a low-dimensional latent embedding of the data and a corresponding fast emulator in the latent space. We demonstrate superior performance over existing methods in a realistic, non-identifiable parameter estimation task.
  arXiv  Detail & Related papers  (2024-09-27T02:37:01Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• Bridging the Sim-to-Real Gap with Bayesian Inference [53.61496586090384]
  We present SIM-FSVGD for learning robot dynamics from data. We use low-fidelity physical priors to regularize the training of neural network models. We demonstrate the effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a high-performance RC racecar system.
  arXiv  Detail & Related papers  (2024-03-25T11:29:32Z)
• Robust Neural Posterior Estimation and Statistical Model Criticism [1.5749416770494706]
  We argue that modellers must treat simulators as idealistic representations of the true data generating process. In this work we revisit neural posterior estimation (NPE), a class of algorithms that enable black-box parameter inference in simulation models. We find that the presence of misspecification, in contrast, leads to unreliable inference when NPE is used naively.
  arXiv  Detail & Related papers  (2022-10-12T20:06:55Z)
• Neural Posterior Estimation with Differentiable Simulators [58.720142291102135]
  We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency.
  arXiv  Detail & Related papers  (2022-07-12T16:08:04Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Auto-Tuned Sim-to-Real Transfer [143.44593793640814]
  Policies trained in simulation often fail when transferred to the real world. Current approaches to tackle this problem, such as domain randomization, require prior knowledge and engineering. We propose a method for automatically tuning simulator system parameters to match the real world.
  arXiv  Detail & Related papers  (2021-04-15T17:59:55Z)
• Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation [62.22058066456076]
  Reinforcement Learning (RL) represents powerful tools to solve complex robotic tasks. RL does not work directly in the real-world, which is known as the sim-to-real transfer problem. We propose a method that learns on an observation space constructed by point clouds and environment randomization.
  arXiv  Detail & Related papers  (2020-07-27T17:46:59Z)
• Leveraging Vision and Kinematics Data to Improve Realism of Biomechanic Soft-tissue Simulation for Robotic Surgery [13.657060682152409]
  We investigate how live data acquired during any robotic endoscopic surgical procedure may be used to correct for inaccurate FEM simulation results. We use an open-source da Vinci Surgical System to probe a soft-tissue phantom and replay the interaction in simulation. We train the network to correct for the difference between the predicted mesh position and the measured point cloud.
  arXiv  Detail & Related papers  (2020-03-14T00:16:08Z)

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.