# GP-ILQG: Data-driven Robust Optimal Control for Uncertain Nonlinear Dynamical Systems

arXiv: https://arxiv.org/abs/1705.05344 · HTML: https://mlsanity.com/paper/1705.05344/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2017.

## Built on by (most cited first, 4 of 4)

- [Bayesian Optimization via Continual Variational Last Layer Training](https://mlsanity.com/paper/2412.09477/) (2024)
- [Driving Through Ghosts: Behavioral Cloning with False Positives](https://mlsanity.com/paper/2008.12969/) (2020)
- [Compositional Learning-based Planning for Vision POMDPs](https://mlsanity.com/paper/2112.09456/) (2021)
- [Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control](https://mlsanity.com/paper/2103.04548/) (2021)

## Abstract (arXiv)

As we aim to control complex systems, use of a simulator in model-based reinforcement learning is becoming more common. However, it has been challenging to overcome the Reality Gap, which comes from nonlinear model bias and susceptibility to disturbance. To address these problems, we propose a novel algorithm that combines data-driven system identification approach (Gaussian Process) with a Differential-Dynamic-Programming-based robust optimal control method (Iterative Linear Quadratic Control). Our algorithm uses the simulator's model as the mean function for a Gaussian Process and learns only the difference between the simulator's prediction and actual observations, making it a natural hybrid of simulation and real-world observation. We show that our approach quickly corrects incorrect models, comes up with robust optimal controllers, and transfers its acquired model knowledge to new tasks efficiently.