Overview
This week delves into the fascinating world of Robotics, exploring how AI and machine learning techniques can be applied to create intelligent robotic systems. We’ll cover fundamental concepts, popular frameworks, and practical applications in the field of robotics.
Instructor
Yixin Zhu, PKU
Topics Covered
- Introduction to ROS (Robot Operating System) and Gazebo
- Overview of OmniVerse and TongVerse
- Fundamentals of motion planning in robotics
- Integration of AI techniques in robotic systems
Assignments
Practice Assignment: Complete a simple robotic task in a simulation environment. You will use various tools and frameworks to understand the principles of robot motion planning. Refer to lab.md for more details.
Written Assignment: Refer to homework.md, and submit a PDF report (written in LaTeX) in your Github classroom repo.
Additional Resources
- ROS Documentation
- Gazebo Tutorials
- NVIDIA Omniverse Documentation
- Introduction to Autonomous Mobile Robots (book)
Notes
- This module combines concepts from previous weeks (especially computer vision and machine learning) with robotics-specific knowledge.
- For the practice assignment, focus on implementing a working solution rather than achieving perfect performance.
- Consider the ethical implications of AI in robotics when writing your paper.
- As always, document your code thoroughly and use version control (Git) for your project.
- Submit your assignments on GitHub Classroom.