Dr. Henrik Christensen Discusses the Future of the Global Robotics Industry

An interview with Dr. Henrik Christensen, TILOS Robotics co-lead and Distinguished Professor of Computer Science at UC San Diego, about the “marriage” of AI and physical automation. Christensen explains why the market’s fixation on sci-fi concepts has created a disconnect, leaving practical robotics companies poised for growth that is not yet priced in. The conversation […]

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Planning & Learning in Robotics

This course covers optimal control fundamentals and their application to motion planning and decision making in robotics. Topics include Markov decision processes (MDPs), dynamic programming, search-based and sampling-based motion planning, value and policy iteration, linear quadratic regulation (LQR), and model-free reinforcement learning.

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Linear Control System Theory

This undergraduate-level course focuses on single-input single-output linear time-invariant control systems emphasizing frequency-domain methods. Topics include modeling of feedback control systems, transient and steady-state behavior, Laplace transforms, stability, root locus, frequency response, Bode plots, Nyquist plots, Nichols plots, PID control, and loop shaping.

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Graph Neural Networks

Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs. They have been developed and are presented in this course as generalizations of the convolutional neural networks (CNNs) that are used to process signals in time and space.

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