用神经网络量化仿真与现实的差距，确保控制算法从模拟到现实平稳迁移。
Quantification of Sim2Real Gap via Neural Simulation Gap Function
- 提出神经仿真差距函数，量化数学模型与高保真仿真间的差异。
- 仅用有限数据训练，却能对全状态空间给出形式化差距保证。
- 适用于机器人控制等需模拟转现实的场景，尤其适合验证控制器鲁棒性。
本文引入神经仿真差距函数，正式量化数学模型与高保真仿真系统之间的差距，后者高度逼近真实世界。由于模型与现实间存在未建模差异，许多为数学模型设计的控制器在现实中无法工作。借助该差距函数，可利用现有基于模型的工具为数学系统设计控制器，并形式化保证其从仿真到现实的可靠迁移。尽管本方法使用有限数据点训练神经网络来量化差距，但仍能对整个状态空间（包括未见数据点）提供形式化保证。数据通过近期实现的“真实到仿真”迁移技术从高保真仿真器中获取，确保与现实高度对齐。我们通过两个案例研究验证：Mecanum底盘和摆锤系统。
原文摘要 · Abstract (English)
In this paper, we introduce the notion of neural simulation gap functions, which formally quantifies the gap between the mathematical model and the model in the high-fidelity simulator, which closely resembles reality. Many times, a controller designed for a mathematical model does not work in reality because of the unmodelled gap between the two systems. With the help of this simulation gap function, one can use existing model-based tools to design controllers for the mathematical system and formally guarantee a decent transition from the simulation to the real world. Although in this work, we have quantified this gap using a neural network, which is trained using a finite number of data points, we give formal guarantees on the simulation gap function for the entire state space including the unseen data points. We collect data from high-fidelity simulators leveraging recent advancements in Real-to-Sim transfer to ensure close alignment with reality. We demonstrate our results through two case studies - a Mecanum bot and a Pendulum.
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