Smarla: A safety monitoring approach for deep reinforcement learning agents
Deep Reinforcement Learning (DRL) has made significant advancements in various fields,
such as autonomous driving, healthcare, and robotics, by enabling agents to learn optimal …
such as autonomous driving, healthcare, and robotics, by enabling agents to learn optimal …
Safety in wearable robotic exoskeletons: Design, control, and testing guidelines
Exoskeletons, wearable robotic devices designed to enhance human strength and
endurance, find applications in various fields such as healthcare and industry; however …
endurance, find applications in various fields such as healthcare and industry; however …
Deepgd: A multi-objective black-box test selection approach for deep neural networks
Deep neural networks (DNNs) are widely used in various application domains such as
image processing, speech recognition, and natural language processing. However, testing …
image processing, speech recognition, and natural language processing. However, testing …
Towards exploring the limitations of test selection techniques on graph neural networks: An empirical study
Abstract Graph Neural Networks (GNNs) have gained prominence in various domains, such
as social network analysis, recommendation systems, and drug discovery, due to their ability …
as social network analysis, recommendation systems, and drug discovery, due to their ability …
Towards building ai-cps with nvidia isaac sim: An industrial benchmark and case study for robotics manipulation
As a representative cyber-physical system (CPS), robotic manipulators have been widely
adopted in various academic research and industrial processes, indicating their potential to …
adopted in various academic research and industrial processes, indicating their potential to …
Common challenges of deep reinforcement learning applications development: an empirical study
Abstract Machine Learning (ML) is increasingly being adopted in different industries. Deep
Reinforcement Learning (DRL) is a subdomain of ML used to produce intelligent agents …
Reinforcement Learning (DRL) is a subdomain of ML used to produce intelligent agents …
Differential safety testing of deep RL agents enabled by automata learning
M Tappler, BK Aichernig - International Conference on Bridging the Gap …, 2023 - Springer
Learning-enabled controllers (LECs) pose severe challenges to verification. Their decisions
often come from deep neural networks that are hard to interpret and verify, and they operate …
often come from deep neural networks that are hard to interpret and verify, and they operate …
Generative model-based testing on decision-making policies
The reliability of decision-making policies is urgently important today as they have
established the fundamentals of many critical applications, such as autonomous driving and …
established the fundamentals of many critical applications, such as autonomous driving and …
Knowledge-enhanced software refinement: leveraging reinforcement learning for search-based quality engineering
MN Abadeh - Automated Software Engineering, 2024 - Springer
In the rapidly evolving software development industry, the early identification of optimal
design alternatives and accurate performance prediction are critical for developing efficient …
design alternatives and accurate performance prediction are critical for developing efficient …
Mosaic: Model-based Safety Analysis Framework for AI-enabled Cyber-Physical Systems
Cyber-physical systems (CPSs) are now widely deployed in many industrial domains, eg,
manufacturing systems and autonomous vehicles. To further enhance the capability and …
manufacturing systems and autonomous vehicles. To further enhance the capability and …