论文标题
校准机器人模拟器的用户指南
A User's Guide to Calibrating Robotics Simulators
论文作者
论文摘要
模拟器是现代机器人研究的关键组成部分。在部署到现实世界系统之前，可以先在模拟中研究感知和决策的策略，从而节省时间和成本。尽管在开发SIM到现实算法方面取得了重大进展，但对不同方法的分析仍以临时方式进行，而没有一致的测试和指标集进行比较。本文填补了这一空白，并提出了一组基准和研究各种算法的框架，旨在将模拟中学到的模型和政策转移到现实世界中。我们对广泛的众所周知的模拟环境进行实验，以表征和提供有关不同算法的性能的见解。我们的分析对于在该领域工作的从业者可能很有用，并且可以帮助对SIM到现实算法的行为和主要特性做出明智的选择。我们可以在https://github.com/nvlabs/sim-parameter-esimation上开放基准，培训数据和训练有素的模型。
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.
