Robots have been highly impactful in controlled factory settings. However, making robots autonomous and useful in uncontrolled unstructured environments requires novel mapping and task planning capabilities. This seminar will discuss representations and algorithms for online 3D mapping of object and semantic information from streaming sensor observations. This capability allows autonomous robots to quickly and meaningfully understand new environments. Given a semantically annotated map, the seminar will also discuss robot task specification, grounding, and planning using automata labeled with object semantics. This capability allows robots to understand and plan tasks specified in natural language by human operators.

Bio:

Nikolay Atanasov is an Assistant Professor of Electrical and Computer Engineering at the University of California San Diego, La Jolla, CA, USA. He obtained a B.S. degree in Electrical Engineering from Trinity College, Hartford, CT, USA in 2008, and M.S. and Ph.D. degrees in Electrical and Systems Engineering from University of Pennsylvania, Philadelphia, PA, USA in 2012 and 2015, respectively. Dr. Atanasov's research focuses on robotics, control theory, and machine learning with emphasis on active perception problems for autonomous mobile robots. He works on probabilistic models and inference techniques for simultaneous localization and mapping (SLAM) and on optimal control and reinforcement learning techniques for autonomous navigation and uncertainty minimization. Dr. Atanasov's work has been recognized by the Joseph and Rosaline Wolf award for the best Ph.D. dissertation in Electrical and Systems Engineering at the University of Pennsylvania in 2015, the Best Conference Paper Award at the IEEE International Conference on Robotics and Automation (ICRA) in 2017, the NSF CAREER Award in 2021, and the IEEE RAS Early Academic Career Award in Robotics and Automation in 2023.