Max Robotics — Global Service Robot Directory + US Certification

About

Jennifer Gielis is a leading researcher in multi-robot systems, with a primary focus on the critical role of communication in enabling coordinated autonomous behavior. Her work bridges robotics and networking, addressing the fundamental challenges of real-world robot-to-robot communication. Gielis’s most impactful contribution is her comprehensive review on communications in multi-robot systems, which has garnered over 125 citations and serves as a foundational resource for the field. She has also pioneered the use of Graph Neural Networks (GNNs) for decentralized policy learning, demonstrating their effectiveness in complex tasks like flocking and cooperative coverage. Notably, Gielis has advanced practical networking protocols by improving 802.11p for safety-critical navigation information exchange in dynamic robot networks. Her recent work introduces CoViS-Net, a decentralized visual spatial foundation model that enables cooperative spatial understanding without centralized processing. Through her research, Gielis has established herself as a key figure in making multi-robot communication both robust and intelligent, directly enabling safer and more capable real-world deployments.