1 citations · 1 across the 4 of their papers we have counts for
5 papers
Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approach
Ammar N. Abbas, Shakra Mehak, Georgios C. Chasparis +4
This study presents a novel methodology incorporating safety constraints into a robotic simulation during the training of deep reinforcement learning (DRL). The framework integrate…
CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation
Aayush Jain, Philip Long, Valeria Villani +2
Mass customization and shorter manufacturing cycles are becoming more important among small and medium-sized companies. However, classical industrial robots struggle to cope with p…
Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies
Ammar N. Abbas, Chidera W. Amazu, Joseph Mietkiewicz +7
In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and appl…
Hierarchical Framework for Interpretable and Probabilistic Model-Based Safe Reinforcement Learning
Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher
The difficulty of identifying the physical model of complex systems has led to exploring methods that do not rely on such complex modeling of the systems. Deep reinforcement learni…
Specialized Deep Residual Policy Safe Reinforcement Learning-Based Controller for Complex and Continuous State-Action Spaces
Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher
Traditional controllers have limitations as they rely on prior knowledge about the physics of the problem, require modeling of dynamics, and struggle to adapt to abnormal situation…