Ensuring Stability in AI-Controlled Systems

Neural networks have revolutionized the design of controllers for robots, making machines more adaptive and efficient. However, their complexity poses challenges in guaranteeing that a robot powered by a neural network will safely accomplish its task. Traditional verification methods, like Lyapunov functions, struggle to scale with complex machines. Researchers from MIT's CSAIL have developed new techniques to rigorously certify Lyapunov calculations in elaborate systems, ensuring stability and safety.

The new algorithm efficiently searches for and verifies a Lyapunov function, providing a stability guarantee for the system. This approach could enable safer deployment of robots and autonomous vehicles, including aircraft and spacecraft. By generating cheaper counterexamples and optimizing the system to account for them, the researchers improved the machine's ability to handle challenging circumstances, ensuring safe operation in a wider range of conditions.

The team simulated a quadrotor drone with lidar sensors stabilizing in a two-dimensional environment. Their algorithm successfully guided the drone to a stable hover position using limited environmental information. This approach also enabled stable operation of other robotic systems, such as an inverted pendulum and a path-tracking vehicle, demonstrating its scalability and effectiveness.

The rigorous use of neural networks as Lyapunov functions requires solving hard global optimization problems. The researchers' novel verification formulation, using a scalable neural network verifier, α,β-CROWN, provides rigorous worst-case scenario guarantees. This work bridges the gap between high performance and the safety guarantees needed for deploying complex neural network controllers in real-world applications.

The stability approach has wide-ranging applications, from autonomous vehicles to drones delivering items or mapping terrains. The techniques developed are general and could assist in other fields, such as biomedicine and industrial processing. The researchers aim to extend their technique to systems with higher dimensions and incorporate data beyond lidar readings, like images and point clouds.

Future research will focus on providing stability guarantees for systems in uncertain environments and subject to disturbances. For instance, ensuring a drone remains stable despite strong gusts of wind. The team also plans to apply their method to optimization problems, minimizing the time and distance a robot needs to complete a task while remaining steady.

The work, supported by Amazon, the National Science Foundation, the Office of Naval Research, and the AI2050 program at Schmidt Sciences, will be presented at the 2024 International Conference on Machine Learning. This research opens exciting directions for further development of optimization algorithms for neural Lyapunov methods and the rigorous use of deep learning in control and robotics.

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