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Job Description

This project will focus on multi-agent exploration and navigation in unknown areas, where multiple autonomous robots (agents) are tasked with spreading over a given region to be explored, or to reach a given position and discover the world to do so as rapidly as possible. Throughout this work, we will assume that robots can communicate their observations (e.g., partial map of the environment) during search, via local or global communications, so they can update their representation of the domain online. These tasks will be approach using generative models (GM), e.g., diffusion models and/or matching flows, to allow agents to generate medium- to longer-term plans that allow them to better trade off exploration and exploitation in the long run.

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

• Good writing/spoken communication skills