Visual Language Agents for Safe and Adaptive Autonomous UAV Operation
This project develops vision language agents that govern drone behaviour in safety critical regulated environments, combining visual perception, language grounded reasoning, and cross episode learning to enable safe and adaptive autonomous operation from simulation to real world deployment. Date: 2024 - 2027 Persons participating in the project:
- PIs: Dr. Francisco Cruz, Dr. Imran Razzak, Dr. Mohammad Deghat
- Associates: Abdulrahman Althobaiti
- Corresponding contact: abdulrahman.althobaiti@unsw.edu.au
- Visual Language Models (VLMs)
- Aerial Robotics / UAVs
- Safety-Critical Autonomy
- Embodied AI
- Human-Robot Interaction
- Sim-to-Real Transfer
| Selected Publications | Web |
|---|---|
| Althobaiti, A., Ayala, A., Gao, J., Almutairi, A., Deghat, M., Razzak, I., & Cruz, F. (2024). How can LLMs and knowledge graphs contribute to robot safety? A few-shot learning approach. In Proceedings of the Australasian Conference on Robotics and Automation (ACRA). 2024. |  |