AI, ML and Friends is a weekly seminar series within the School of Computing on Artificial Intelligence, Machine Learning, and related topics. We are open to attendees and presenters external to the school. Please sign up to the mailing list to receive weekly announcements including zoom details, and email the seminar organiser to schedule a talk.
Upcoming Seminars #
05 October 2022, 11:00 #
Explainable Robotic Systems in Reinforcement Learning Scenarios #
Speaker: Dr. Francisco Cruz
Abstract: Robotic systems are more present in our society everyday. In human-robot environments, it is crucial that end-users may correctly understand their robotic team-partners, in order to collaboratively complete a task. To increase action understanding, users demand more explainability about the decisions by the robot in particular situations. Recently, explainable robotic systems have emerged as an alternative focused not only on completing a task satisfactorily but also on justifying, in a human-like manner, the reasons that lead to making a decision. In reinforcement learning scenarios, a great effort has been focused on providing explanations using data-driven approaches, particularly from the visual input modality in deep learning-based systems. This talk focuses rather on the decision-making process of reinforcement learning agents performing a task in robotic scenarios.
Bio: Francisco Cruz received a bachelor’s degree in engineering and a master’s degree in computer engineering from the University of Santiago, Chile, and a Ph.D. degree from the University of Hamburg, Germany working in developmental robotics focused on interactive reinforcement learning. Previously, Francisco was a Visiting Researcher within the Emergent Robotics Laboratory, Osaka University, Japan, and a Visiting Researcher within the Polytechnic School, University of Pernambuco, Brazil. Currently, he is with the Computer Science and Engineering School at UNSW Sydney, Australia as a Lecturer in Cognitive Robotics. He has published more than 50 research papers in high-ranked journals and international conferences. His research interests include reinforcement learning, explainable artificial intelligence, human-robot interaction, artificial neural networks, and psychologically and bio-inspired models.
Where: Building 145, room 1.33