Neural Network Verification for Gliding Drone Control: A Case Study (2505.00622v1)

Abstract: As machine learning is increasingly deployed in autonomous systems, verification of neural network controllers is becoming an active research domain. Existing tools and annual verification competitions suggest that soon this technology will become effective for real-world applications. Our application comes from the emerging field of microflyers that are passively transported by the wind, which may have various uses in weather or pollution monitoring. Specifically, we investigate centimetre-scale bio-inspired gliding drones that resemble Alsomitra macrocarpa diaspores. In this paper, we propose a new case study on verifying Alsomitra-inspired drones with neural network controllers, with the aim of adhering closely to a target trajectory. We show that our system differs substantially from existing VNN and ARCH competition benchmarks, and show that a combination of tools holds promise for verifying such systems in the future, if certain shortcomings can be overcome. We propose a novel method for robust training of regression networks, and investigate formalisations of this case study in Vehicle and CORA. Our verification results suggest that the investigated training methods do improve performance and robustness of neural network controllers in this application, but are limited in scope and usefulness. This is due to systematic limitations of both Vehicle and CORA, and the complexity of our system reducing the scale of reachability, which we investigate in detail. If these limitations can be overcome, it will enable engineers to develop safe and robust technologies that improve people's lives and reduce our impact on the environment.

• The paper examines verifying neural network controllers for bio-inspired gliding drones, detailing challenges with continuous dynamics and infinite time horizons.
• The study found robust training improved controller stability, but current verification tools proved limited in handling complex continuous systems and infinite time horizons.
• The study highlights the need for enhanced verification tools capable of handling continuous-time dynamics and the complexities of real-world autonomous systems.

Overview of "Neural Network Verification for Gliding Drone Control: A Case Study"

The paper "Neural Network Verification for Gliding Drone Control: A Case Study" by Colin Kessler et al. examines the verification of neural network controllers in the context of bio-inspired micro-drones. These drones, inspired by the Alsomitra macrocarpa seed, are designed to glide, taking advantage of natural wind patterns for extended flights, which can be useful for environmental monitoring applications.

Context and Challenges

Neural networks (NNs) have demonstrated potential in controlling systems with uncertain dynamics, particularly in aerospace applications. This paper focuses on Alsomitra-inspired drones that navigate using engineered neural networks. The verification of such controllers is essential to ensure safety and robustness, especially when these drones interact with humans, other air users, and the environment.

The verification task poses unique challenges:

1. Continuous Dynamics: Unlike discrete-time benchmarks, Alsomitra drones require consideration of continuous flight dynamics.
2. Complex Equations: The drones' behavior is governed by nonlinear differential equations with complexities surpassing existing verification benchmarks like ARCH-COMP's QUAD.
3. Trajectory-based Safety: The concept of safety is not binary (safe/unsafe states) but rather adheres to maintaining a defined trajectory.
4. Infinite Time Horizon: Drones leveraging wind for sustained flight necessitate an infinite-time horizon for verification.

Methodology

The verification process involves two primary tasks:

1. Ensuring the NN-controller does not deviate significantly from the target trajectory.
2. Verifying that drones from a given initial set reach a goal region after a predefined finite time.

The paper proposes robust training techniques for regression networks, focusing on Lipschitz robustness to enhance the controllers' stability. The authors also utilize a combination of verification tools, specifically Marabou and CORA, to handle the complex interactions between continuous equations and neural network models.

Results and Implications

The results from the paper highlight several insights:

• Robust Training: Implementing adversarial training methods improved robustness, suggesting that neural networks can better handle perturbations and uncertainties.
• Tool Limitations: Current verification tools exhibit structural limitations, primarily when addressing complex systems with sophisticated mathematical models and real-world dynamics. For example, Vehicle and Marabou require enhancements for more complex input constraints and infinite time-horizon considerations.
• Exploration of Properties: The paper evaluates several properties regarding the adherence of drones to a trajectory, examining their feasibility and limitations under current technology paradigms.

Future Directions

There are considerable implications for future research and development:

1. The necessity of integration and enhancement of existing verification systems to handle continuous-time dynamics and complex real-world applications.
2. Expanding the research on property-driven training and its effects on verification, particularly in more generalized frameworks.
3. Delving deeper into the balance between robustness and precision in the training of neural networks as controllers.

The paper concludes that while current verification technology presents challenges, these obstacles provide a roadmap for future tool development. As such, continuous innovation in this space will be instrumental in advancing safe, sustainable, and effective bio-inspired drone technologies for environmental and monitoring applications.

This paper is vital for advancing verification methodologies to cater to continuously evolving autonomous systems, highlighting the gap and need for more robust verification tools capable of handling the intricacies of cyber-physical systems.