Introduction to Robotics @ Princeton

Lectures from "Introduction to Robotics" at Princeton University (MAE/ECE 345, COS 346, MAE 549).
Instructor: Anirudha Majumdar (irom-lab.princeton.edu/majumdar).
Other course materials (notes, slides, etc.): https://irom-lab.princeton.edu/intro-to-robotics

Course description:
Robotics is a rapidly growing field with applications including unmanned aerial vehicles, autonomous cars, and robotic manipulators. This course will provide an introduction to the fundamental theoretical and algorithmic principles behind robotic systems. The course will also allow students to get hands-on experience through project-based assignments with the Crazyflie quadrotor. Topics include:

Feedback Control
Motion Planning
State estimation, localization, and mapping
Computer vision and learning
Broader topics: Robotics and the law, ethics, and economics

This course is aimed at undergraduate students (primarily juniors and seniors). The graduate-level track (MAE 549) is aimed at first-year PhD students.

Lecture 24: Princeton: Introduction to Robotics | "Robotics and the economy, ethics, and laws"

Lecture 23: Princeton: Introduction to Robotics | "Reinforcement Learning"

Lecture 22: Princeton: Introduction to Robotics | "Convolutional neural networks"

Lecture 21: Princeton: Introduction to Robotics | "Overfitting and regularization"

Lecture 20: Princeton: Introduction to Robotics | "Stochastic gradient descent"

Lecture 19: Princeton: Introduction to Robotics | "Intro to deep learning for vision"

Lecture 18: Princeton: Introduction to Robotics | "Optical Flow"

Lecture 17: Princeton: Introduction to Robotics | "Intro to Vision"

Lecture 16: Princeton: Introduction to Robotics | "SLAM"

Lecture 15: Princeton: Introduction to Robotics | "Mapping"

Lecture 14: Princeton: Introduction to Robotics | "Localization"

Lecture 13: Princeton: Introduction to Robotics | "Particle filters and Kalman filters"

Lecture 12: Princeton: Introduction to Robotics | "Bayes Filtering"

Lecture 11: Princeton: Introduction to Robotics | "The Nondeterministic Filter"

Lecture 10: Princeton: Introduction to Robotics | "Planning with dynamics constraints"

Lecture 8: Princeton: Introduction to Robotics | "Randomized motion planning (RRTs)"

Lecture 9: Princeton: Introduction to Robotics | "Differential flatness"

Lecture 7: Princeton: Introduction to Robotics | "Optimal Discrete Planning"

Lecture 5: Princeton: Introduction to Robotics | "Linear Quadratic Regulator (LQR)"

Lecture 6: Princeton: Introduction to Robotics | "Discrete Planning (BFS and DFS)"

Lecture 3: Princeton: Introduction to Robotics | "Feedback Control"

Lecture 4: Princeton: Introduction to Robotics | "Stability and PD Control"

Lecture 2: Princeton: Introduction to Robotics | "Dynamics"

Lecture 1: Princeton: Introduction to Robotics