13 citations · 20 across the 10 of their papers we have counts for
4 papers · 1 filter
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:…
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…
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…