MS in Robotics

Curriculum for self-paced robotics master's degree that includes courses and projects. This curriculum covers fundamental concepts, advanced topics, and practical projects in robotics.

Semester 1:

1. Course: Introduction to Robotics by Stanford University (Coursera)

   • Explore the basics of robotics, including kinematics, dynamics, control, and perception.

2. Course: Robot Mechanics and Control by ETH Zurich (edX)

   • Study advanced topics in robot mechanics and control, including dynamics modeling and inverse kinematics.

3. Course: Machine Learning by Stanford University (Coursera)

   • Learn the fundamentals of machine learning techniques and algorithms.

4. Project: Implement a robot arm control system using inverse kinematics algorithms and apply machine learning algorithms for task optimization.

Semester 2:

1. Course: Autonomous Mobile Robots by ETH Zurich (edX)

   • Focus on autonomous robot navigation, localization, and mapping algorithms.

2. Course: Probabilistic Graphical Models by Stanford University (Coursera)

   • Study probabilistic graphical models and their applications in robotics.

3. Course: Computer Vision by Georgia Institute of Technology (Udacity)

   • Explore computer vision techniques and algorithms for robotic perception.

4. Project: Develop a mobile robot capable of autonomous navigation and mapping in a simulated environment using computer vision and probabilistic techniques.

Semester 3:

1. Course: Reinforcement Learning by University of Alberta (Coursera)

   • Deepen your understanding of reinforcement learning algorithms and their applications.

2. Course: Robot Perception by University of Pennsylvania (Coursera)

   • Dive into advanced topics in robot perception, including simultaneous localization and mapping (SLAM) and visual odometry.

3. Course: Robotic Manipulation by University of Pennsylvania (edX)

   • Explore techniques for robotic manipulation, including grasp planning and object recognition.

4. Project: Develop a robot capable of grasping and manipulating objects using reinforcement learning algorithms for control and perception.

Semester 4:

1. Course: Robot Motion Planning by University of Freiburg (Coursera)

   • Study advanced motion planning algorithms for autonomous robots.

2. Course: Deep Learning Specialization by deeplearning.ai (Coursera)

   • Gain expertise in deep learning techniques relevant to robotics.

3. Course: Human-Robot Interaction by University of Colorado Boulder (Coursera)

   • Explore principles and techniques for designing effective human-robot interaction.

4. Project: Design and implement an autonomous robot capable of navigating complex environments, interacting with humans, and performing complex tasks using motion planning and deep learning techniques.

Semester 5:

1. Course: Robot Localization and Mapping by University of Freiburg (Coursera)

   • Deepen your knowledge of localization and mapping algorithms for mobile robots.

2. Course: Advanced Topics in Robotics by Carnegie Mellon University (YouTube)

   • Watch lectures and seminars on advanced robotics topics offered by Carnegie Mellon University.

3. Course: Robot Ethics and Responsible Innovation by Delft University of Technology (edX)

   • Examine ethical considerations and responsible practices in robotics and artificial intelligence.

4. Project: Undertake a research-oriented project in a specific area of robotics, focusing on advanced localization and mapping techniques or an emerging robotics research topic.

Note: This curriculum's suggested outline may not perfectly align with the specific courses offered at University. Additionally, it's important to check the availability and prerequisites of each course on the respective platforms before enrolling.