Dr Francisco Cruz
UNSW
Francisco Cruz received a bachelor’s and a master’s degree from the University of Santiago, Chile, in 2004 and 2006, respectively, and a Ph.D. degree from the University of Hamburg, Germany, in 2017. His work focused on developmental robotics and particularly on interactive reinforcement learning. In 2015, Dr Cruz was a Visiting Researcher within the Emergent Robotics Laboratory, Osaka University and in 2018, a Visiting Researcher within the Polytechnic School, University of Pernambuco, Brazil. He was previously a postdoctoral fellow at Deakin University and joined UNSW in 2022 as a Lecturer in Cognitive Robotics. His current research interests include reinforcement learning, explainable artificial intelligence, human-robot interaction, artificial neural networks, and psychologically and bio-inspired models.
ARAAC Publications
- (2025). Understanding user preferences in explainable artificial intelligence: a mapping function proposal ACM Transactions on Intelligent Systems and Technology
- (2025). AI apology: a critical review of apology in AI systems Artificial Intelligence Review
- (2024). Boosting reinforcement learning algorithms in continuous robotic reaching tasks using adaptive potential functions Australasian Joint Conference on Artificial Intelligence (AI 2024)
- (2024). Elastic step DQN: A novel multi-step algorithm to alleviate overestimation in Deep Q-Networks Neurocomputing
- (2023). Elastic step DDPG: Multi-step reinforcement learning for improved sample efficiency International Joint Conference on Neural Networks (IJCNN)
- (2023). Towards a broad-persistent advising approach for deep interactive reinforcement learning in robotic environments Sensors
- (2023). Human engagement providing evaluative and informative advice for interactive reinforcement learning Neural Computing and Applications
- (2023). Persistent rule-based interactive reinforcement learning Neural Computing and Applications
- (2023). Human-aligned reinforcement learning for autonomous agents and robots Neural Computing and Applications
- (2023). Explainable reinforcement learning for broad-XAI: a conceptual framework and survey Neural Computing and Applications
- (2023). AI apology: interactive multi-objective reinforcement learning for human-aligned AI Neural Computing and Applications
- (2022). Analysis of explainable goal-driven reinforcement learning in a continuous simulated environment Algorithms
- (2022). Evaluating human-like explanations for robot actions in reinforcement learning scenarios IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- (2021). A robust approach for continuous interactive actor-critic algorithms IEEE Access
- (2021). An evaluation methodology for interactive reinforcement learning with simulated users Biomimetics
- (2021). A conceptual framework for externally-influenced agents: an assisted reinforcement learning review Journal of Ambient Intelligence and Humanized Computing
- (2021). Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario Neural Computing and Applications
- (2021). Levels of explainable artificial intelligence for human-aligned conversational explanations Artificial Intelligence
- (2020). Deep reinforcement learning with interactive feedback in a human-robot environment Applied Sciences
- (2019). Memory-based explainable reinforcement learning Australasian Joint Conference on Artificial Intelligence (AI 2019)