Safety Assessment for Autonomous Driving
POMDP-based safety assessment for autonomous driving systems using realistic adversarial trajectory generation under uncertainty.
View projectResearch Fellow · Australian National University
Enabling robots to learn, plan, and act in uncertain worlds.
My current research focuses on multi-agent reinforcement learning and integrated learning and planning methods for robots operating in complex, uncertain environments. During my PhD, I developed a user-centric assessment framework for evaluating and ranking the relative safety and effectiveness of autonomous robotic systems through adversarial testing before real-world deployment.
- Multi-agent RL
- Integrated Learning + Planning
- Safety Assessment
- Robust Decision-Making
- Robotic Systems
Featured Projects
AuSRoS Hands-on Workshop: High-level Planning to Low-level Control
Hands-on workshop material connecting RRT/MCTS planning with learned locomotion policies in Isaac Sim and Isaac Lab.
View projectAUREL: Augmented Reality Learning
An AR learning platform that combines mobile vision, cloud ML, and 3D interaction to make STEM concepts more tangible.
View projectLatest News
Jul 2026
I will be presenting a poster at Robotics Science and Systems (RSS) From Imitation to Certification workshop.
Jul 2026
I will be presenting a hands-on workshop on Planning with Learnt Locomotion in Isaac Sim at AuSRoS 2026.
Mar 2026
I have joined ANU Robotics as a Research Fellow, where I work on multi-agent reinforcement learning, integrated learning and planning, and embodied robotic systems for intent-aware decision-making under uncertainty.
Jun 2025
I will be joining the Robotic Autonomy Team at CSIRO Data61 as a research intern, working on evaluating the safety and robustness of robotic manipulator systems.