13 citations · 20 across the 10 of their papers we have counts for
12 papers
Formally Guaranteed Control Adaptation for ODD-Resilient Autonomous Systems
Gricel Vázquez, Calum Imrie, Sepeedeh Shahbeigi +5
Ensuring reliable performance in situations outside the Operational Design Domain (ODD) remains a primary challenge in devising resilient autonomous systems. We explore this challe…
Learning to Navigate Under Imperfect Perception: Conformalised Segmentation for Safe Reinforcement Learning
Daniel Bethell, Simos Gerasimou, Radu Calinescu +1
Reliable navigation in safety-critical environments requires both accurate hazard perception and principled uncertainty handling to strengthen downstream safety handling. Despite t…
Safe But Not Sorry: Reducing Over-Conservatism in Safety Critics via Uncertainty-Aware Modulation
Daniel Bethell, Simos Gerasimou, Radu Calinescu +1
Ensuring the safe exploration of reinforcement learning (RL) agents is critical for deployment in real-world systems. Yet existing approaches struggle to strike the right balance:…
Conformal Safety Shielding for Imperfect-Perception Agents
William Scarbro, Calum Imrie, Sinem Getir Yaman +4
We consider the problem of safe control in discrete autonomous agents that use learned components for imperfect perception (or more generally, state estimation) from high-dimension…
Assuring the Safety of Reinforcement Learning Components: AMLAS-RL
Calum Corrie Imrie, Ioannis Stefanakos, Sepeedeh Shahbeigi +2
The rapid advancement of machine learning (ML) has led to its increasing integration into cyber-physical systems (CPS) across diverse domains. While CPS offer powerful capabilities…
RESMETRIC: Analyzing Resilience to Enable Research on Antifragility
Ferdinand Koenig, Marc Carwehl, Calum Imrie
A key feature in self-adaptive systems is resilience, which is an ongoing research topic. Recently, the community started to explore antifragility, which describes the improvement…
Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
Daniel Bethell, Simos Gerasimou, Radu Calinescu +1
Empowering safe exploration of reinforcement learning (RL) agents during training is a critical challenge towards their deployment in many real-world scenarios. When prior knowledg…
Formal Synthesis of Uncertainty Reduction Controllers
Marc Carwehl, Calum Imrie, Thomas Vogel +3
In its quest for approaches to taming uncertainty in self-adaptive systems (SAS), the research community has largely focused on solutions that adapt the SAS architecture or behavio…
Analyzing and Debugging Normative Requirements via Satisfiability Checking
Nick Feng, Lina Marsso, Sinem Getir Yaman +11
As software systems increasingly interact with humans in application domains such as transportation and healthcare, they raise concerns related to the social, legal, ethical, empat…
Bayesian Learning for the Robust Verification of Autonomous Robots
Xingyu Zhao, Simos Gerasimou, Radu Calinescu +3
Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments. As such, they must continually verify the…
Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study
Corina S. Pasareanu, Ravi Mangal, Divya Gopinath +4
Deep neural networks (DNNs) are increasingly used in safety-critical autonomous systems as perception components processing high-dimensional image data. Formal analysis of these sy…
Discrete-Event Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components
Radu Calinescu, Calum Imrie, Ravi Mangal +4
We present DeepDECS, a new method for the synthesis of correct-by-construction discrete-event controllers for autonomous systems that use deep neural network (DNN) classifiers for…