Related papers: Modular Verification of Autonomous Space Robotics

Autonomous robots must utilize rich sensory data to make safe control decisions. To process this data, compute-constrained robots often require assistance from remote computation, or the cloud, that runs compute-intensive deep neural…

Safety and mission performance validation of autonomous vehicles (AVs) is a major challenge. In this paper we describe a methodology for constructing and applying assertion checks to validate the behaviour of an AV operating either in…

Software system certification presents itself with many challenges, including the necessity to certify the system at the level of functional requirements, code and binary levels, the need to chase down run-time errors, and the need for…

As robotic systems such as autonomous cars and delivery drones assume greater roles and responsibilities within society, the likelihood and impact of catastrophic software failure within those systems is increased.To aid researchers in the…

While LLM-based agents are able to tackle a wide variety of code reasoning questions, the answers are not always correct. This prevents the agent from being useful in situations where high precision is desired: (1) helping a software…

As autonomy becomes prevalent in many applications, ranging from recommendation systems to fully autonomous vehicles, there is an increased need to provide safety guarantees for such systems. The problem is difficult, as these are large,…

In order to properly train a machine learning model, data must be properly collected. To guarantee a proper data collection, verifying that the collected data set holds certain properties is a possible solution. For example, guaranteeing…

Formal specification is widely employed in the construction of high-quality software. However, there is often a huge gap between formal specification and actual implementation. While there is already a vast body of work on software testing…

The dependency on the correct functioning of embedded systems is rapidly growing, mainly due to their wide range of applications, such as micro-grids, automotive device control, health care, surveillance, mobile devices, and consumer…

We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components. VERIFAI particularly seeks to address challenges with applying formal…

Algorithmic assurances from advanced autonomous systems assist human users in understanding, trusting, and using such systems appropriately. Designing these systems with the capacity of assessing their own capabilities is one approach to…

The execution failure of cyber-physical systems (e.g., autonomous driving systems, unmanned aerial systems, and robotic systems) could result in the loss of life, severe injuries, large-scale environmental damage, property destruction, and…

This paper describes the development and verification of a competitive parachute system for Micro Air Vehicles, in particular focusing on verification of the embedded software. We first introduce the overall solution including a system…

In situations such as habitat construction, station inspection, or cooperative exploration, incorrect assumptions about the environment or task across the team could lead to mission failure. Thus it is important to resolve any ambiguity…

First-order optimization methods have attracted a lot of attention due to their practical success in many applications, including in machine learning. Obtaining convergence guarantees and worst-case performance certificates for first-order…

This paper surveys how formal verification can be used to prove the correctness of ad hoc routing protocols, which are fundamental infrastructure of wireless sensor networks. The existing techniques fall into two classes: verification on…

Cloud solutions are increasingly used for a plethora of purposes, including solving memory-intensive and computation-intensive problems. Ensuring the reliability, availability, scalability, and security of cloud solutions, as networked…

Researchers have recently designed a number of application-specific fault tolerance mechanisms that enable applications to either be naturally resilient to errors or include additional detection and correction steps that can bring the…

The current certification process for aerospace software is not adapted to "AI-based" algorithms such as deep neural networks. Unlike traditional aerospace software, the precise parameters optimized during neural network training are as…

Testing of autonomous systems is extremely important as many of them are both safety-critical and security-critical. The architecture and mechanism of such systems are fundamentally different from traditional control software, which appears…