Human-Agent Interaction
Autonomous agents are most useful when they can learn from and work alongside people. ARAAC researchers study the interaction between humans and agents, including interactive and human-in-the-loop reinforcement learning, human-machine collaboration, and ways for agents to leverage human knowledge to learn more efficiently and behave in socially responsible ways.
Key Researchers
Dr Francisco Cruz
UNSW
Francisco’s current research interests include reinforcement learning, explainable artificial intelligence, human-robot interaction, artificial neural networks, and psychologically and bio-inspired models.
Hadassah Harland (Haddie)
Deakin University
Haddie is a PhD student at Deakin University (Geelong) and Top-Up Scholarship recipient with CSIRO’s Data61 Robotics and Autonomous Systems Group, with an interest in Human-Machine Collaboration.
Dr Thommen George Karimpanal
Deakin University
Thommen is a Lecturer at Deakin University. His research interests lie in the field of reinforcement learning, and in improving its sample efficiency by leveraging available context, be it in the form of domain priors, knowledge from humans or other agents in the loop and/or inferring patterns from the environment itself. Overall, his research aims to develop efficient, aligned and practically deployable algorithms.
ARAAC Publications
- (2025). BCR-DRL: Behavior- and Context-Aware Reward for Deep Reinforcement Learning in Human-AI Coordination European Conference on Artificial Intelligence (ECAI)
- (2025). Human-informed skill discovery: Controlled diversity with preference in reinforcement learning Expert Systems with Applications
- (2025). Human-Aligned Skill Discovery: Balancing Behaviour Exploration and Alignment International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
- (2024). Personalisation via Dynamic Policy Fusion International Conference on Human-Agent Interaction (HAI)
- (2024). EMOTE: An Explainable Architecture for Modelling the Other through Empathy International Joint Conference on Artificial Intelligence (IJCAI)
- (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). AI apology: interactive multi-objective reinforcement learning for human-aligned AI Neural Computing and Applications
- (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). 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