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Dr. Francisco Cruz
Group Leader
Autonomous Agents and Robotics Research Group
UNSW Sydney, Australia

Address
School of Computer Science and Engineering
Ainsworth Building (J17) - Room 510J
Kensington Campus
UNSW Sydney
NSW 2052, Australia

Contact
Phone: +61 2 9348 0597
Email: f.cruz@unsw.edu.au

Research interests

  • Artificial Intelligence, Artificial Neural Networks, Machine Learning, Cognitive and Developmental Robotic, Bioinspired Models, Explainable Artificial Intelligence
  • Reinforcement Learning, Contextual Affordances, Dynamic Models, Grey Box Neural Models
  • Interactive Reinforcement Learning, Explainable Reinforcement Learning, Human-Robot Interaction, Multimodal Integration

Useful links

Short bio: I received a bachelor's degree in engineering and a master's degree in computer engineering from the University of Santiago, Chile, in 2004 and 2006, respectively, and a Ph.D. degree from the University of Hamburg, Germany, in 2017, working in developmental robotics focused on interactive reinforcement learning.

In 2015, I 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. I joined UNSW in 2022 as a Lecturer in Cognitive Robotics. My current research interests include reinforcement learning, explainable artificial intelligence, human-robot interaction, artificial neural networks, and psychologically and bio-inspired models.

Selected Publications Web
Mesto, M., & Cruz, F. (2025, November). The Consensus Paradox: When Low Disagreement Leads to Catastrophic Failure in Multi-teacher Reinforcement Learning. In Australasian Joint Conference on Artificial Intelligence, (pp. 426-438). Singapore: Springer Nature Singapore. Best paper award.
Cruz, F., Dazeley, R., Vamplew, P., & Moreira, I. (2023). Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario. Neural Computing and Applications, 35(25), 18113-18130.
Bignold, A., Cruz, F., Taylor, M. E., Brys, T., Dazeley, R., Vamplew, P., & Foale, C. (2023). A conceptual framework for externally-influenced agents: An assisted reinforcement learning review. Journal of Ambient Intelligence and Humanized Computing, 14(4), 3621-3644.
Cruz, F., Young, C., Dazeley, R., & Vamplew, P. (2022, October). Evaluating human-like explanations for robot actions in reinforcement learning scenarios. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (pp. 894-901). IEEE.
Dazeley, R., Vamplew, P., Foale, C., Young, C., Aryal, S., & Cruz, F. (2021). Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence, 299, 103525.
Ayala, A., Cruz, F., Campos, D., Rubio, R., Fernandes, B., & Dazeley, R. (2020, October). A comparison of humanoid robot simulators: A quantitative approach. In Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), (pp. 1-6). IEEE. Most popular presentation.
Cruz, F., Magg, S., Nagai, Y., & Wermter, S. (2018). Improving interactive reinforcement learning: What makes a good teacher?. Connection Science, 30(3), 306-325.
Cruz, F., Magg, S., Weber, C., & Wermter, S. (2016). Training agents with interactive reinforcement learning and contextual affordances. IEEE Transactions on Cognitive and Developmental Systems, 8(4), 271-284.
Cruz, F., Parisi, G. I., Twiefel, J., & Wermter, S. (2016, October). Multi-modal integration of dynamic audiovisual patterns for an interactive reinforcement learning scenario. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (pp. 759-766). IEEE.