Neural implicit representations have emerged as a powerful tool for robotic scene reconstruction but deploying them across multi-robot teams introduces a fundamental challenge: robots in the real world cannot always communicate reliably. This talk presents a line of research that addresses this challenge through a unifying principle of uncertainty-aware distributed optimization, progressing from handling asynchronous communication, to surviving extreme communication degradation, to enabling continual mapping over time. Through extensive experiments on benchmark datasets and real-world robot hardware, we demonstrate that by explicitly accounting for the reliability of each robot’s map during the collaboration process, multi-robot teams can build accurate and consistent neural maps of their environment even when communication is severely limited, bringing collaborative neural mapping meaningfully closer to real-world deployment.
Negar Mehr is an assistant professor in the Department of Mechanical Engineering at the University of California, Berkeley. Previously, she was an assistant professor of Aerospace Engineering at the University of Illinois Urbana-Champaign. Before that, she was a postdoctoral scholar at Stanford Aeronautics and Astronautics department. She received her Ph.D. in Mechanical Engineering from UC Berkeley in 2019 and her B.Sc. in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, in 2013. She is a recipient of the NSF CAREER Award and ONR Young Investigator Program (YIP) award. She was recently recognized as a rising star by the American Society of Mechanical Engineers (ASME). She was awarded the IEEE Intelligent Transportation Systems best Ph.D. dissertation award in 2020.
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