# Dropout MPC: An Ensemble Neural MPC Approach for Systems with Learned Dynamics

arXiv: https://arxiv.org/abs/2406.02497 · HTML: https://mlsanity.com/paper/2406.02497/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [What can linearized neural networks actually say about generalization?](https://mlsanity.com/paper/2106.06770/) (2021)
- [A Survey on Ensemble Learning under the Era of Deep Learning](https://mlsanity.com/paper/2101.08387/) (2021)
- [Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms](https://mlsanity.com/paper/2203.07747/) (2022)
- [Fast Adaptation with Linearized Neural Networks](https://mlsanity.com/paper/2103.01439/) (2021)
- [Deep Learning Tubes for Tube MPC](https://mlsanity.com/paper/2002.01587/) (2020)
- [Perceptual Attention-based Predictive Control](https://mlsanity.com/paper/1904.11898/) (2019)

## Abstract (arXiv)

Neural networks are lately more and more often being used in the context of data-driven control, as an approximate model of the true system dynamics. Model Predictive Control (MPC) adopts this practise leading to neural MPC strategies. This raises a question of whether the trained neural network has converged and generalized in a way that the learned model encapsulates an accurate approximation of the true dynamic model of the system, thus making it a reliable choice for model-based control, especially for disturbed and uncertain systems. To tackle that, we propose Dropout MPC, a novel sampling-based ensemble neural MPC algorithm that employs the Monte-Carlo dropout technique on the learned system model. The closed loop is based on an ensemble of predictive controllers, that are used simultaneously at each time-step for trajectory optimization. Each member of the ensemble influences the control input, based on a weighted voting scheme, thus by employing different realizations of the learned system dynamics, neural control becomes more reliable by design. An additional strength of the method is that it offers by design a way to estimate future uncertainty, leading to cautious control. While the method aims in general at uncertain systems with complex dynamics, where models derived from first principles are hard to infer, to showcase the application we utilize data gathered in the laboratory from a real mobile manipulator and employ the proposed algorithm for the navigation of the robot in simulation.