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Job Description

This project focuses on developing deep multi-agent reinforcement learning approaches for a team of multi-rotor drones to collaboratively search for targets in low-rise urban environments. These environments are usually characterized by cluttered structures, GNSS-denied conditions, low-light scenarios, and real-world constraints such as limited onboard computation and communication bandwidth. The candidate will investigate both conventional pipelines, including mapping and motion planning, and recent AI-based methods to design robust, scalable, and efficient swarm planning strategies. Each drone will be equipped with LiDAR and camera sensors for mapping and navigation, potentially enhanced by semantic perception to improve decision-making and coordination. The candidate will develop both simulation environment for training AI policy and deploy the controller on hardware.

Qualifications

• Excellent coding skills in python with pytorch (distributed deep reinforcement learning, Transformers, etc.)

• Literature review/summarizing skills

• Simulations abilities (e.g., Isaac Lab, ROS Gazebo)

• Experience with implementation of deep learning model on aerial robots is preferred

• Experience publishing papers, and supervising undergraduate/master's students

• Good writing/spoken communication skills