Neural network controllers provide stability for complex robots, paving the way for safer deployments of autonomous vehicles and industrial machines. Researchers at the Massachusetts Institute of Technology have developed an efficient algorithm to authenticate the Lyapunov function in complex systems, ensuring the stability and safety of neural network-controlled robots in a variety of environments.
Neural networks have had a huge impact on how engineers design robot controllers, giving rise to more adaptable and efficient machines. However, these brain-like machine learning systems are also a double-edged sword: their complexity makes them powerful, but they also struggle to ensure that robots powered by neural networks can do their tasks safely.
The traditional way to verify security and stability is through a technique called the Lyapunov function. If you can find a Lyapunov function with a consistently decreasing value, then you can know that the insecurity or instability associated with higher values will never occur. However, for robots controlled by neural networks, the previous methods for verifying Lyapunov conditions did not scale well to complex machines.
Researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and other institutions have now developed new technologies that allow for rigorous certification of Lyapunov computing in more complex systems. The algorithm efficiently searches and verifies the Lyapunov function, which provides a stability guarantee for the system. This approach has the potential to make the deployment of robots and autonomous vehicles, including airplanes and spacecraft, safer.
To outperform previous algorithms, the researchers found a money-saving shortcut in the training and validation process. They generate lower-cost counterexamples-for example, adversarial data from sensors that could disable controllers-and then optimize the robotic system to deal with those counterexamples. Understanding these edge cases helps machine learning handle challenging situations, allowing them to operate safely in a wider range of conditions than ever before. They then developed a novel verification formula capable of using a scalable neural network validator α, β-CROWN, to provide strict worst-case guarantees in addition to counterexamples.
"We've seen some impressive empirical performance on AI-controlled machines such as humanoid robots and robot dogs, but these AI controllers lack the formal assurance that is critical for safety-critical systems." "Our work closes the gap between the level of performance of neural network controllers and the security guarantees required to deploy more complex neural network controllers in the real world," said Lujie Yang, a PhD student in electrical engineering and computer science (EECS) at the Massachusetts Institute of Technology and a CSAIL affiliated researcher. "
In a digital demonstration, the team simulated how a quadcopter drone with a lidar sensor would be stable in a two-dimensional environment. Their algorithm successfully steers the drone to a stable hovering position, using only the limited environmental information provided by the lidar sensor.
In two other experiments, their approach enabled two simulated robotic systems to operate stably under a wider range of conditions: an inverted pendulum and a path-following vehicle. These experiments, while small, are much more complex than previous neural network validations might do, especially since they include sensor models.
"Unlike common machine learning problems, the strict use of neural networks as Lyapunov functions requires solving difficult global optimization problems, so scalability is a key bottleneck," said Sicun Gao, associate professor of computer science and engineering at the University of California, San Diego.
It offers significant improvements in scalability and solution quality compared to existing methods. This work opens up exciting directions for the further development of optimization algorithms for the neural Lyapunov method, as well as the rigorous use of deep learning in control and robotics.
The new stability method has the potential to be widely applied. In these applications, safety is paramount. It can help ensure that self-driving cars such as airplanes and spacecraft drive more smoothly. Similarly, drones that deliver items or map different terrains can also benefit from this security guarantee.
The new approach is not limited to robotics and may help other applications in the future, such as biomedical and industrial processing. While the technology is an improvement over previous work in terms of scalability, researchers are exploring how it can perform better in systems with higher dimensions. They also want to consider data beyond lidar readings, such as images and point clouds.
As a future research direction, the team hopes to provide the same stability guarantee for systems in uncertain environments and susceptible to interference. For example, if a drone is exposed to a strong gust of wind, researchers want to make sure it still flies stably and accomplishes its intended mission.
In addition, they intend to apply their methods to optimization problems, with the goal of minimizing the time and distance required for the robot to complete the task while maintaining stability. and plans to expand their technology to humanoid robots and other real-world machines where the robot needs to be stable when in contact with its surroundings.
