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.

翻译：黑盒模拟器在机器人学中应用广泛，但由于似然函数不可访问，其参数优化仍具挑战性。基于模拟的推理（SBI）通过模拟驱动的方法应对此问题，利用离线真实观测和前向模拟估计后验分布。然而，在黑盒场景中，由于参数与观测之间的关系未知，难以准备包含足够信息用于参数估计的观测数据。本文提出主动模拟推理（ASBI），一种参数估计框架，利用机器人主动收集真实世界在线数据，以实现精确的黑盒模拟器调优。我们的框架通过最大化信息增益来优化机器人动作以收集信息丰富的观测，信息增益定义为后验与先验之间香农熵的期望减少量。虽然计算信息增益需要似然函数，这在黑盒模拟器中不可访问，但我们的方法通过利用神经后验估计（NPE）解决了这一问题，该方法利用神经网络学习后验估计器。三个模拟实验定量验证了我们的方法实现了精确的参数估计，后验分布紧密集中在真实参数周围。此外，我们展示了一个实际应用：使用真实机器人通过倾倒动作，估计对应于两种真实物体（珠子和砾石）的立方体颗粒的模拟参数。