Activity Monitoring under Occlusion Conditions Activity Monitoring under Occlusion Conditions. PIs: Dr. Francisco Cruz, Dr. Eduardo Benitez Sandoval, Prof. Erik Meijering Associates: Marsha Mariya Kappan
Augmented Reality Assistance for Drone Wayfinding and Precision Landing on Specific Point Target Augmented Reality Assistance for Drone Wayfinding and Precision Landing on Specific Point Target. PIs: Dr. Francisco Cruz, Dr. Imran Razzak Associates: Abdulrahman Althobaiti
Applications of Spiking Neural Networks in Robotics Applications of Spiking Neural Networks in Robotics. PIs: Dr. Francisco Cruz, A/Prof. Leo Wu Associates: Oltan Sevinc
Emotional Sensitivity in Human-Computer Interaction (HCI) for Explainability upon Decision-Making Reasoning Emotional Sensitivity in Human-Computer Interaction (HCI) for Explainability upon Decision-Making Reasoning. PIs: Dr. Francisco Cruz, Prof. Flora Salim, Dr. Benjamin Tag Associates: Chris Lee
Multimodal robot learning via proactive human-robot collaboration Multimodal robot learning via proactive human-robot collaboration. PIs: Dr. Francisco Cruz, Dr Shadi Abpeikar, A/Prof. Vidhyasaharan Sethu Associates: Hadha Afrisal
Adaptive Ensemble Weighting for Online Reinforcement Learning Adaptive Ensemble Weighting for Online Reinforcement Learning. PIs: Dr. Francisco Cruz, Dr. Eduardo Benitez Sandoval, Prof. Richard Dazeley, Prof. Peter Vamplew Associates: Charlie Stinson
Exploring Multi-Agent Human Interaction Scenarios in Reinforcement Learning: Coordination, Conflict Resolution, and User Experience Exploring Multi-Agent Human Interaction Scenarios in Reinforcement Learning: Coordination, Conflict Resolution, and User Experience. PIs: Dr. Francisco Cruz, Dr. Pamela Carreno-Medrano, Prof. Claude Sammut Associates: Maher Mesto
Robust Skill Chaining In Hierarchical Reinforcement Learning Robust Skill Chaining In Hierarchical Reinforcement Learning. PIs: A/Prof. Gelareh Mohammadi, Prof. Arcot Sowmya, Dr. Francisco Cruz Associates: Madeleine Nouri
Teaching With Interactive Reinforcement Learning (TWIRL) Reinforcement Learning has been a very useful approach, but often works slowly, because of large-scale exploration. A variant of RL, that tries to improve speed of convergence, and that has been rarely used until now is Interactive Reinforcement Learning (IRL), that is, RL is supported by a human trainer who gives some directions on how to tackle the problem. PIs: Prof. Dr. Stefan Wermter Associates: Dr. Francisco Cruz, Dr. Sven Magg, Dr. Cornelius Weber Date: July 2013 - July 2017