Discovering Closed-Loop Failures of Vision-Based Controllers via Reachability Analysis

Core Concepts

Closed-loop failures of vision-based controllers can be systematically discovered by casting the problem as a Hamilton-Jacobi reachability analysis, blending simulation-based methods to overcome the challenge of lacking analytical models.

Abstract

The paper proposes a framework that combines formal guarantees of Hamilton-Jacobi (HJ) reachability analysis with simulation-based methods to discover closed-loop failures of vision-based controllers.
The key idea is to cast the problem of finding closed-loop vision failures as an HJ reachability problem. This allows computing the Backward Reachable Tube (BRT) - the set of all initial states that will eventually reach an unsafe state under the vision-based controller. The sequences of visual inputs corresponding to the states in the BRT can then be classified as the inputs that result in closed-loop system failures.
To overcome the challenge of lacking analytical models relating the system state to the visual input, the approach blends level set-based reachability methods with simulation-based techniques. Level set methods can compute the BRT numerically over a state-space grid, only requiring the system dynamics at the grid points. The authors leverage readily available photo-realistic simulators to obtain the visual inputs and control inputs at the grid points, enabling BRT computation without an analytical model of the environment.
The framework is demonstrated on two case studies: (1) an autonomous aircraft taxiing task using an RGB image-based neural network controller, and (2) an autonomous indoor navigation task using a vision-based controller. The analysis of the obtained BRTs uncovers various failure modes of the vision-based controllers, such as failures near the boundary of the runway, failures due to asymmetric camera placement, and failures due to the presence of runway markings. The authors also show that their reachability-based approach can systematically discover these failures more efficiently compared to forward simulation-based methods.

Stats

The paper does not contain any explicit numerical data or statistics. The key insights are derived from the analysis of the computed Backward Reachable Tubes (BRTs) and the corresponding visual inputs that lead to closed-loop failures.

Quotes

"Existing methods leverage simulation-based testing (or falsification) to find the failures of vision-based controllers, i.e., the visual inputs that lead to closed-loop safety violations. However, these techniques do not scale well to the scenarios involving high-dimensional and complex visual inputs, such as RGB images."
"Our key idea in overcoming this challenge is to blend level set-based reachability methods with simulation-based methods to compute a numerical approximation of the BRT."
"Utilizing the BRT, we can tractably and systematically find the system states and corresponding visual inputs that lead to closed-loop failures."

Key Insights Distilled From

Discovering Closed-Loop Failures of Vision-Based Controllers via Reachability Analysis

by Kaustav Chak... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2211.02736.pdf

Deeper Inquiries

How can the proposed framework be extended to handle partially observable environments where the robot's state is not fully known

In partially observable environments where the robot's state is not fully known, the proposed framework can be extended by incorporating techniques from reinforcement learning, specifically addressing the problem of Partially Observable Markov Decision Processes (POMDPs). By integrating POMDP solvers or algorithms into the framework, the system can account for uncertainty in the robot's state estimation and make decisions based on probabilistic beliefs about the state. This extension would involve modeling the robot's belief state, which captures the uncertainty about the true state given the observations. The framework can then utilize POMDP solvers to compute policies that maximize expected rewards under this belief state representation, enabling the robot to make informed decisions even in partially observable environments.

What are the potential limitations of the HJ reachability-based approach, and how can it be combined with other techniques like adversarial training to further improve the robustness of vision-based controllers

The HJ reachability-based approach, while powerful in analyzing closed-loop failures of vision-based controllers, may have limitations in handling complex, high-dimensional systems with intricate dynamics. One potential limitation is the computational complexity associated with solving the HJB-VI for large state spaces, which can become prohibitive for real-time applications. To address this, combining the HJ reachability-based approach with adversarial training techniques can enhance the robustness of vision-based controllers. Adversarial training can be used to generate diverse and challenging scenarios that expose vulnerabilities in the controller, which can then be analyzed using the HJ reachability framework. By iteratively training the controller on both nominal and adversarial scenarios identified through reachability analysis, the system can learn to adapt to a wider range of inputs and improve its resilience to unforeseen failures.

Can the insights gained from the failure analysis be used to guide the design of more robust vision-based controllers, for example, by incorporating additional sensor modalities or modifying the training process

Insights gained from the failure analysis can indeed guide the design of more robust vision-based controllers by informing the incorporation of additional sensor modalities or modifications to the training process. For example, the failure analysis may reveal specific scenarios where the vision-based controller struggles, such as near obstacles or in low-visibility conditions. This information can be used to identify the limitations of relying solely on visual inputs and motivate the integration of complementary sensor modalities, such as depth sensors or LiDAR, to provide more comprehensive environmental information. Additionally, the failure analysis can guide modifications to the training process by emphasizing the generation of training data that covers a diverse set of challenging scenarios identified through the failure analysis. By training the controller on a more comprehensive dataset that includes failure cases, the system can learn to handle a wider range of situations and improve its overall robustness.