How can intelligent machines perceive, make decisions, and execute their plans in an uncertain, dynamic world? This course will cover algorithms for robotic perception, planning, and control with a focus on real-time adaptation and learning. Students should have prior experience with methods in signal processing and machine learning. Topics covered include probabilistic methods for scene segmentation, multimodal sensory integration, latent variable models for dynamical systems, path planning, and reinforcement learning for motor control.