Providing Safety Assurances for Systems with Unknown Dynamics: A Deep Learning Approach

Core Concepts

The author aims to provide safety assurances for systems with unknown dynamics by leveraging deep learning techniques and ensemble models to handle model uncertainty effectively.

Abstract

The content discusses the challenges of providing safety assurances for systems with unknown dynamics. It introduces a method that uses deep learning techniques and ensemble models to address model uncertainty effectively. The approach is demonstrated through simulated case studies and hardware experiments, showing robust control actions against model uncertainty while generating safe behaviors without being overly restrictive.

Stats

In the past decade, there has been tremendous success in using deep learning techniques to model and control systems.
We learn an ensemble model of the system dynamics from data.
Our method is agnostic to how the nominal model is obtained or the model uncertainty quantification.
For all the experiments in this letter, we take α = γ = 3.
We train an ensemble dynamics model containing 5 fully connected feed-forward neural networks.

Quotes

"Our method robustifies the control actions of the system against model uncertainty."
"The safe set from our method is entirely contained in the ground truth safe set."
"Our method utilizes state-dependent model uncertainty bounds."

Key Insights Distilled From

Providing Safety Assurances for Systems with Unknown Dynamics

by Hao Wang,Jav... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.05771.pdf

Deeper Inquiries

How can higher-dimensional systems be addressed within this framework

In addressing higher-dimensional systems within this framework, one approach could be to incorporate learning-based reachability computation tools. By leveraging tools like DeepReach, which utilizes deep learning for high-dimensional reachability analysis, the framework can scale effectively to handle systems with more complex dynamics and a larger state space. These tools can help in efficiently computing the value function and determining robust safe sets even in higher dimensions. Additionally, techniques such as abstraction and decomposition of the system dynamics can aid in managing the complexity of higher-dimensional systems within the safety assurance framework.

What are potential limitations of estimating model uncertainties using this approach

One potential limitation of estimating model uncertainties using this approach is that it may not accurately reflect the true distribution of realized model uncertainties. The method outlined in the context relies on bounding model uncertainties based on standard deviations from an ensemble of neural networks. However, these bounds might not capture all variations or correlations among different models within the ensemble accurately. This could lead to overly conservative or optimistic estimates of uncertainty, impacting the robustness and effectiveness of safety assurances provided by the framework.

How can other uncertainty estimation approaches enhance the accuracy of realized model uncertainties

To enhance the accuracy of realized model uncertainties within this approach, alternative uncertainty estimation methods like Bayesian approximations through dropout layers in neural networks can be explored. By implementing dropout as a Bayesian approximation technique, models can better represent uncertainty by capturing variations during training and inference stages effectively. This would provide a more nuanced understanding of model uncertainty across different states and inputs compared to simple standard deviation-based approaches used traditionally with ensembles.