Catalog Description: This course introduces fundamental concepts, algorithms, and state-of-the-art methods for probabilistic and learning-based robot perception, inference, and localization, with emphasis on multi-modal sensor fusion for autonomous embodied AI systems such as aerial and ground robots. It covers probabilistic state estimation and their extension to modern SLAM frameworks. The course also addresses learning-based predictive models for robotic inference, including neural networks and Gaussian Processes for uncertainty-aware prediction, motion forecasting, planning, and control. A laboratory component includes hands-on experiments and a final project requirement.

Also Offered As: EECS 116

Grading Basis: Default Letter Grade; P/NP Option

Final Exam Status: No

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