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Job Description
This project will focus on the multi-agent search problem, where multiple autonomous robots (agents) are tasked with spreading over a given region to be explored, to locate given targets of interest trying to evade detection. The number of targets is unknown, and their initial position is not known as well. There might be obstacles in the domain, and their location might or might not be known. Throughout this work, we will assume that robots can communicate their observations (presence/absence of targets) during search, via local or global communications, so they can update their representation of the domain and likely location(s) of targets (more details about this representation in the next Section). Our goal is to allow robots to plan efficient search paths to cover the domain and localise the targets as quickly as possible, while relying on local sensing and on distributed intelligence rather than on centralised planning, which would represent a vulnerable bottleneck and would not scale well to large agent teams.
Qualifications
• Excellent coding skills in python with pytorch (distributed deep reinforcement learning, Transformers, etc.)
• Literature review/summarizing skills
• Simulations abilities (e.g., AirSIM, ROS Gazebo)
• Experience with implementation of deep learning model on hardware (e.g., ground/aerial robots)
• Experience publishing papers, and supervising undergraduate/master’s students
