Advancing Towards Safer and Trustworthy Autopilots for Flying | MIT News
Introduction:
The researchers at MIT have developed a new technique that can solve complex stabilize-avoid problems, surpassing existing methods in terms of safety and stability. This machine-learning approach, which combines constrained optimization and deep reinforcement learning, allows autonomous aircraft to navigate extreme scenarios and avoid collisions with obstacles. In a simulated test, the technique successfully piloted a jet aircraft through a narrow corridor without crashing. The researchers believe that their approach could be applied to designing controllers for highly dynamic robots or integrated into larger systems to enhance safety and stability. Further enhancements are being explored to account for uncertainty and real-world dynamics.
Full Article: Advancing Towards Safer and Trustworthy Autopilots for Flying | MIT News
MIT Researchers Develop Technique to Solve Complex Stabilize-Avoid Problems
In the movie “Top Gun: Maverick,” Tom Cruise’s character Maverick trains young pilots to execute a challenging mission involving flying jets undetected through a rocky canyon. While human pilots can accomplish this task with Maverick’s help, autonomous aircraft struggle due to the conflict between the most straightforward path and the need to avoid colliding with canyon walls or staying undetected. MIT researchers have now developed a machine-learning approach that can solve these complex stabilize-avoid problems more effectively than existing methods. Their technique provides a tenfold increase in stability while matching or exceeding the safety of current approaches.
Breaking Down the Stabilize-Avoid Problem
Traditional methods for tackling stabilize-avoid problems simplify the system to solve it using straightforward math. However, these simplified results often fail to hold up in real-world dynamics. More effective techniques, such as reinforcement learning, involve trial and error with rewards for behavior that gets the agent closer to the goal. The MIT researchers approached the problem in two steps:
1. Reframing the stabilize-avoid problem as a constrained optimization problem.
2. Reformulating the constrained optimization problem into the epigraph form and solving it using a deep reinforcement learning algorithm.
By combining these steps, the researchers were able to overcome the challenges faced by other methods.
Testing and Results
The researchers conducted control experiments with different initial conditions to test their approach. Their technique proved to be the only one able to stabilize all trajectories while maintaining safety. In a simulated scenario resembling a “Top Gun” movie, the researchers successfully piloted a jet aircraft to stabilize near the ground, maintain a low altitude, and stay within a narrow flight corridor. Their technique outperformed all baselines in terms of stability and safety.
Future Applications
The researchers believe their technique could serve as a starting point for designing controllers for highly dynamic robots, including autonomous delivery drones. It could also be integrated into larger systems, activating only when needed, such as assisting drivers in navigating dangerous road conditions. The team plans to enhance their technique to consider uncertainty and investigate its performance when deployed on hardware.
Promising First Step
Stanley Bak, an assistant professor at Stony Brook University, commends the MIT researchers for improving reinforcement learning performance in dynamical systems where safety matters. Their technique ensures that controllers not only reach their target but also maintain safety and stability. The research has received funding from MIT Lincoln Laboratory under the Safety in Aerobatic Flight Regimes program.
Conclusion
MIT researchers have developed a machine-learning approach to solve complex stabilize-avoid problems more effectively. By reframing the problem as a constrained optimization problem and reformulating it using deep reinforcement learning, the researchers achieved a tenfold increase in stability while maintaining safety. Their technique was successful in piloting a simulated jet aircraft through extreme scenarios, showcasing its potential for designing controllers for highly dynamic robots and mission-critical systems. further advancements will focus on incorporating uncertainty and testing the technique on hardware.
Summary: Advancing Towards Safer and Trustworthy Autopilots for Flying | MIT News
MIT researchers have developed a new technique using machine learning that can solve complex stabilize-avoid problems, such as flying a jet through a narrow corridor without crashing into the ground. Traditionally, many methods simplified the problem to solve it with straightforward math, but the results often did not hold up in real-world dynamics. The researchers reframed the problem as a constrained optimization problem and used a deep reinforcement learning algorithm to solve it. Their approach was able to stabilize all trajectories while maintaining safety, outperforming all other baselines. This technique could be applied to designing controllers for autonomous delivery drones or to assist drivers in navigating extreme scenarios.
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