Kalman Filtering

Markov Decision Process (MDP)

Contractions

Asynchronous value iteration

Linear–quadratic regulator (LQR)

Differential dynamic programming (DDP)

Quadruped locomotion

Partially observable Markov decision process (POMDP)

Bandits

Separation Principle

Dynamics Modeling

Policy Gradient

Temporal difference (TD) learning

State–action–reward–state–action (SARSA)

TD-Gammon

Reward Shaping

Linear Programming Approach

Inverse reinforcement learning

Model Predictive Control

Simultaneous Localization and Mapping (SLAM)

Linearly-solvable Markov decision problems

Learning to walk

Q-learning

Function approximation

Exploration / Exploitation

Dynamic programming

Policy Iteration

CS 294-40: Learning for robotics and control UC Berkeley Fall 2008 This advanced course focuses on the applications of machine learning in the robotics and control field. It covers a wide range of topics including Markov Decision Processes, control theories, estimation methodologies, and robotics principles. Recommended for graduate students. This is an advanced course in learning for robotics and control. The goal of this course is to help the audience with their research in learning for robotics and control or related topics. A tentative list of topics includes:

- Markov decision processes: value iteration, policy iteration, linear programming, Q learning, TD, value function approximation, inverse reinforcement learning
- Control: linear quadratic regulator, differential dynamic programming, receding horizon / model predictive control
- Estimation: (extended) Kalman filters, particle filters, SLAM
- Robotics: basic principles of various robots, sensors, microcontrollers
- Exploration/Exploitation: bandits, no-regret, e^3  Familiarity with mathematical proofs, machine learning, artificial intelligence, optimization, probability, algorithms, linear algebra; ability to implement algorithmic ideas in code (C/C++ and matlab).

Graduate students only (consent of instructor required for undergraduate students, please talk to me after first lecture and hand me summary of relevant classes/experience so I can decide whether to make an exception). 

CS 294-40: Learning for robotics and control

Robotics

Hardware

Safety

Linux

Networking

Middleware

Sensors

Inertial measurement unit (IMU)

Time-of-flight (ToF) camera

Camera

Measuring Velocity and Position

Proportional–integral–derivative (PID) controller

Uncertainty

CS1951R Introduction to Robotics Brown University Fall 2023 This course offers students the opportunity to build and program an autonomous drone. Focusing primarily on autonomous drones, the course provides a broader insight into modern robotics, encompassing autonomous ground vehicles and robotic arms. Topics include safety, networking, controls, state estimation, and high-level planning. By the end, students can design, build, and operate a robotic drone. The aim of this course is to empower people to build robots. Students will build, program, and fly an autonomous drone. We will cover everything needed to program an autonomous robot, including safety, networking, state estimation, controls, and high-level planning. Although the course focuses on an autonomous drone, we will provide a broad overview of modern robotics, including some topics relating to autonomous ground vehicles and robotic arms. Each student will build and program their own small quadcopter. The course will provide basic components and a small number of replacement parts. After taking this course, students will be able to:

- Explain the space of designs for robotic communications, safety, state estimation, and control.
- Apply that knowledge to construct programs for communications, safety, state estimation, and control.
- Build, program, and operate an autonomous robot drone.
  The class textbook can be found online at [Duckiesky Learning Materials](https://docs.duckietown.org/daffy/doc-sky/out/index.html). Each assignment or project is a link to a specific chapter in this textbook and has corresponding lectures in the edX Edge course.

CS1951R Introduction to Robotics

Kalman filter

Kalman Filtering

2 courses cover this concept

CS 294-40: Learning for robotics and control

CS1951R Introduction to Robotics