Spring 2026 GRASP on Robotics: Nikolay Atanasov, University of California San Diego, “Elements of Generalizable Robot Autonomy”
March 6 at 10:30 AM - 11:45 AM
This event will be in-person ONLY in Wu and Chen Auditorium.
ABSTRACT
Recent years have seen a transformation in artificial intelligence fueled by the convergence of machine learning models, internet-scale data, and large training infrastructures. Vision-Language Models (VLMs) have enabled unprecedented progress in aligned vision-language processing, while Vision-Language-Action (VLA) models and deep reinforcement learning (RL) have dominated the synthesis of intelligent robot behavior. Yet, most VLA and RL methods are model-free, relying on raw image sequences and expert demonstrations to make decisions. This raises concerns regarding scaling to complex tasks, which benefits from extended spatial and temporal context, and generalization to new operational conditions, which benefits from modular understanding of robot, environment, and task properties.
This seminar explores model-based techniques for robot behavior synthesis that integrate robot, environment, and task models, constructed from sensor observations. First, we present a physics-informed approach for learning robot models using neural ordinary differential equations that guarantee energy conservation and kinematic constraints by construction. Next, we focus on learning metric-semantic environment models from RGB and depth observations using implicit neural features. Finally, we discuss learning task models as automata labeled with observation features and trained from demonstrations. We evaluate our techniques in autonomous robot navigation and manipulation examples.
Nikolay Atanasov
University of California, San Diego
Nikolay Atanasov is an Associate Professor in the Department 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 robot 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.