# A User's Guide to Calibrating Robotics Simulators

arXiv: https://arxiv.org/abs/2011.08985 · HTML: https://mlsanity.com/paper/2011.08985/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [Robot Learning from Randomized Simulations: A Review](https://mlsanity.com/paper/2111.00956/) (2021)
- [DROPO: Sim-to-Real Transfer with Offline Domain Randomization](https://mlsanity.com/paper/2201.08434/) (2022)
- [A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation](https://mlsanity.com/paper/2112.05068/) (2021)
- [DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting](https://mlsanity.com/paper/2203.10263/) (2022)
- [GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models](https://mlsanity.com/paper/2608.05948/) (2026)
- [Online vs. Offline Adaptive Domain Randomization Benchmark](https://mlsanity.com/paper/2206.14661/) (2022)
- [BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym](https://mlsanity.com/paper/2107.04527/) (2021)
- [Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review](https://mlsanity.com/paper/2307.08024/) (2023)
- [Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review](https://mlsanity.com/paper/2505.07911/) (2025)
- [Enhancing Sim2Real Transfer for Torque-Controlled Robots through Real2Sim Dynamics Estimation and Reinforcement Learning](https://mlsanity.com/paper/2608.22629/) (2026)

## Abstract (arXiv)

Simulators are a critical component of modern robotics research. Strategies for both perception and decision making can be studied in simulation first before deployed to real world systems, saving on time and costs. Despite significant progress on the development of sim-to-real algorithms, the analysis of different methods is still conducted in an ad-hoc manner, without a consistent set of tests and metrics for comparison. This paper fills this gap and 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. We open-source the benchmark, training data, and trained models, which can be found at https://github.com/NVlabs/sim-parameter-estimation.