DateWeek DayClassTopicReadingsHW outHW in
8/211M1Introduction to roboticsCh1, Ch2  
8/23 W2Motion planning for point robotsCh8, Ch9.1-3, C.1, C.2HW1 
8/282M3Mobile robot planning, C-spaceCh9.4-6, Ch.11.1  
8/30 W4Mobile robot planning, C-space, cont
9/6 W5Sampling-based motion planningCh10.1-3HW2HW1
9/114M6Sampling-based motion planning pt 2Ch10.4-6
9/13 W7Kinodynamic motion planningCh11.1-2
9/185M8Kinodynamic motion planning (cont)
9/20 W9Trajectory optimizationCh17.1,17.4-6HW3HW2
9/256M10Trajectory optimization pt 2Ch17.1,17.4-6
9/27 W11Constrained trajectory optimization
10/27M12Constrained trajectory optimization (cont)
10/4 W13Real time planning & controlHW3
10/98M14State estimation and uncertaintyA3, PR 2.1-3HW4
10/11 W15Probabilistic Gaussian filteringPR 2.4-6, 3.1. An Introduction to the Kalman Filter
10/169M16Probabilistic filteringPR 3.2-3, An Introduction to the Kalman Filter
10/18 W17Particle filteringPR 4 
10/2310M18System ID and predictionHW5HW4
10/25 W19System ID and prediction, cont 
10/3011M20Rigid registrationCVAA 11.1-3 
11/1 W213D mappingPR 7.1-4
11/612M22SLAMPR 7.5-6, 8.1-3 HW6HW5
11/8 W23Integrating planning and perception
11/1313M24Planning under uncertainty
11/15 W25Planning with partial observability 
11/22 W   
11/2715M26Reinforcement learning  HW6
11/30 W27Reinforcement learning, cont   
12/4 16M28Learning-based planning  
 12/6 W29Task and motion planning   


HW1    Geometric motion planning
HW2    Sampling-based and kinodynamic planning
HW3    Trajectory optimization and optimal control
HW4    Probability
HW5    Probabilistic filtering
HW6    SLAM and 3D mapping

Take-home final: choice of planning / perception