Robotics

Robotics is a multidisciplinary field that combines elements of computer science, mechanical and electrical engineering, and more. In the realm of computer science, robotics is often focused on aspects such as robot programming, artificial intelligence (AI), machine learning, and autonomous systems. This can encompass things like perception (how a robot understands its environment), planning (how a robot decides what actions to take), and control (how a robot executes these actions).

Prerequisites

Before jumping into robotics, you should have a good understanding of several key areas:

Mathematics foundations including Linear Algebra, Calculus and Probability Theory.
Computer Programming such Python and C++
Background in Artificial Intelligence and Machine Learning is a plus.

Common concepts

Kalman Filtering Markov Decision Process (MDP)Simultaneous Localization and Mapping (SLAM)TensorFlow Time-of-flight (ToF) camera Inertial measurement unit (IMU)State–action–reward–state–action (SARSA)Asynchronous value iteration Shared autonomy Networking Sensors Hardware Camera Learning to walk Differential dynamic programming (DDP)Dynamic programming Measuring Velocity and Position Exploration / Exploitation Linux Dynamics Modeling Proportional–integral–derivative (PID) controller Middleware Q-learning Bandits Quadruped locomotion Grasping and Manipulation Interaction-aware learning Policy Gradient Reinforcement learning (RL)Linearly-solvable Markov decision problems Reward Shaping Interactive Perception Partially observable Markov decision process (POMDP)Learning-based grasping and manipulation Inverse reinforcement learning Contractions Model-based and model-free RL for robot control Linear–quadratic regulator (LQR)TD-Gammon Policy Iteration Uncertainty Temporal difference (TD) learning Imitation learning Safety Separation Principle Reinforcement Learning from Human Feedback (RLHF)Model Predictive Control Linear Programming Approach Neural network Learning-based perception Function approximation

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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.

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AA 174B / AA 274B / CS 237B / EE 260B Principles of Robot Autonomy II

Stanford University

Winter 2023

This course provides a deeper understanding of robot autonomy principles, focusing on learning new skills and physical interaction with the environment and humans. It requires familiarity with programming, ROS, and basic robot autonomy techniques.

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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.

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