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Weekly Learning Plan for Physical AI & Humanoid Robotics

Course Overview​

This 12-week intensive course provides a comprehensive journey through Physical AI and Humanoid Robotics, integrating theoretical concepts with practical implementation. Each week builds upon the previous one, following the "Embodied Intelligence" framework that connects perception, reasoning, and action in robotic systems.

Learning Objectives​

By the end of this course, students will be able to:

  • Design and implement robot control systems using ROS 2
  • Create simulation environments for robot testing and training
  • Integrate perception systems using NVIDIA Isaac
  • Implement Vision-Language-Action models for human-robot interaction
  • Configure and deploy robot systems on appropriate hardware platforms

Prerequisites​

  • Basic programming skills (Python preferred)
  • Understanding of linear algebra and calculus
  • Familiarity with Linux command line
  • Basic knowledge of robotics concepts (helpful but not required)

Week-by-Week Breakdown​

Week 1: Introduction to Physical AI & ROS 2 Fundamentals​

Learning Outcomes​

  • Understand the concept of embodied intelligence
  • Install and configure ROS 2 environment
  • Create basic ROS 2 nodes and topics
  • Work with the command line tools

Topics Covered​

  • Introduction to Physical AI and embodied intelligence
  • ROS 2 architecture and concepts (nodes, topics, services)
  • Setting up development environment
  • Creating your first ROS 2 package
  • Understanding the ROS 2 build system

Assignments​

  • Set up ROS 2 Humble Hawksbill environment
  • Create a publisher-subscriber system
  • Implement a simple robot controller node

Resources​

  • ROS 2 documentation and tutorials
  • ROS 2 installation guide
  • Basic ROS 2 concepts video series

Assessment​

  • Quiz on ROS 2 concepts (20%)
  • Basic publisher-subscriber implementation (10%)

Week 2: Advanced ROS 2 Concepts and URDF​

Learning Outcomes​

  • Create robot models using URDF
  • Implement services and actions
  • Use ROS 2 launch files
  • Understand robot state management

Topics Covered​

  • URDF (Unified Robot Description Format)
  • ROS 2 services and actions
  • Launch files and parameters
  • TF (Transform) system
  • Robot state publisher

Assignments​

  • Create a URDF model for a simple robot
  • Implement a service server and client
  • Create launch files for robot simulation

Resources​

  • URDF tutorials
  • ROS 2 services and actions documentation
  • TF tutorials

Assessment​

  • URDF model implementation (15%)
  • Service implementation (10%)

Week 3: Simulation with Gazebo​

Learning Outcomes​

  • Create Gazebo simulation environments
  • Configure robot models for simulation
  • Implement sensor simulation
  • Test control algorithms in simulation

Topics Covered​

  • Gazebo world files and SDF format
  • Robot plugins for Gazebo
  • Sensor simulation (cameras, LiDAR, IMU)
  • Physics configuration and parameters
  • ROS 2 integration with Gazebo

Assignments​

  • Create a custom Gazebo world
  • Configure a robot model for simulation
  • Implement sensor simulation for your robot

Resources​

  • Gazebo tutorials
  • ROS 2 Gazebo integration guide
  • Physics engine documentation

Assessment​

  • Gazebo world implementation (15%)
  • Robot simulation setup (10%)

Week 4: Perception Systems and Computer Vision​

Learning Outcomes​

  • Implement basic computer vision algorithms
  • Process sensor data in ROS 2
  • Use OpenCV with ROS 2
  • Understand sensor fusion concepts

Topics Covered​

  • Image processing fundamentals
  • OpenCV integration with ROS 2
  • Camera calibration
  • Point cloud processing
  • Sensor fusion techniques

Assignments​

  • Implement object detection in ROS 2
  • Calibrate a camera sensor
  • Process LiDAR data for obstacle detection

Resources​

  • OpenCV tutorials
  • ROS 2 vision tutorials
  • Sensor fusion documentation

Assessment​

  • Computer vision implementation (15%)
  • Sensor processing project (10%)

Week 5: Navigation and Path Planning​

Learning Outcomes​

  • Configure ROS 2 navigation stack
  • Implement path planning algorithms
  • Understand SLAM concepts
  • Test navigation in simulation

Topics Covered​

  • ROS 2 Navigation 2 (Nav2)
  • Costmap configuration
  • Global and local planners
  • SLAM (Simultaneous Localization and Mapping)
  • Recovery behaviors

Assignments​

  • Configure navigation stack for your robot
  • Implement a custom path planner
  • Test navigation in Gazebo environment

Resources​

  • Nav2 tutorials
  • Path planning algorithms documentation
  • SLAM implementation guides

Assessment​

  • Navigation stack configuration (15%)
  • Path planning implementation (10%)

Week 6: NVIDIA Isaac Introduction​

Learning Outcomes​

  • Install and configure Isaac ROS
  • Understand Isaac Sim capabilities
  • Implement GPU-accelerated perception
  • Integrate Isaac with ROS 2

Topics Covered​

  • Isaac ROS packages overview
  • GPU-accelerated computer vision
  • Isaac Sim for robotics simulation
  • Isaac ROS-GPU integration
  • Performance optimization

Assignments​

  • Set up Isaac ROS environment
  • Implement GPU-accelerated perception node
  • Create Isaac Sim environment

Resources​

  • Isaac ROS documentation
  • Isaac Sim tutorials
  • GPU optimization guides

Assessment​

  • Isaac ROS setup (10%)
  • GPU-accelerated perception (15%)

Week 7: Isaac Perception and SLAM​

Learning Outcomes​

  • Implement advanced perception with Isaac
  • Use Isaac's SLAM capabilities
  • Optimize perception pipelines
  • Integrate perception with navigation

Topics Covered​

  • Isaac perception pipelines
  • VSLAM (Visual SLAM) with Isaac
  • Deep learning inference acceleration
  • Multi-sensor fusion with Isaac
  • Performance benchmarking

Assignments​

  • Implement Isaac-based VSLAM
  • Optimize perception pipeline for real-time performance
  • Integrate perception with navigation

Resources​

  • Isaac perception tutorials
  • VSLAM documentation
  • Performance optimization guides

Assessment​

  • VSLAM implementation (15%)
  • Perception optimization (10%)

Week 8: Manipulation and Control​

Learning Outcomes​

  • Implement robot manipulation algorithms
  • Use Isaac for manipulation
  • Understand inverse kinematics
  • Integrate manipulation with perception

Topics Covered​

  • Inverse kinematics (IK) solvers
  • Motion planning for manipulation
  • Isaac manipulation tools
  • Grasp planning algorithms
  • Force control concepts

Assignments​

  • Implement IK solver for robotic arm
  • Create manipulation pipeline
  • Integrate perception and manipulation

Resources​

  • IK solver documentation
  • Manipulation tutorials
  • Isaac manipulation guides

Assessment​

  • IK implementation (15%)
  • Manipulation pipeline (10%)

Week 9: Vision-Language-Action Models Introduction​

Learning Outcomes​

  • Understand VLA model concepts
  • Explore pre-trained VLA models
  • Implement basic VLA pipeline
  • Connect language understanding to actions

Topics Covered​

  • VLA model architecture
  • Vision-language integration
  • Action space mapping
  • Pre-trained model usage
  • Training data requirements

Assignments​

  • Explore existing VLA models
  • Implement basic VLA pipeline
  • Connect language commands to simple actions

Resources​

  • VLA model documentation
  • Research papers on VLA models
  • Implementation tutorials

Assessment​

  • VLA model exploration (10%)
  • Basic VLA implementation (15%)

Week 10: Advanced VLA Models and Integration​

Learning Outcomes​

  • Fine-tune VLA models for specific tasks
  • Integrate VLA with robot control
  • Implement closed-loop VLA systems
  • Evaluate VLA performance

Topics Covered​

  • VLA model fine-tuning
  • Real-time VLA inference
  • Safety considerations for VLA
  • Performance evaluation metrics
  • Human-robot interaction with VLA

Assignments​

  • Fine-tune VLA model for specific task
  • Implement closed-loop VLA control
  • Evaluate VLA performance on robot

Resources​

  • Fine-tuning guides
  • Performance evaluation tools
  • Safety implementation guides

Assessment​

  • VLA fine-tuning (15%)
  • Closed-loop implementation (10%)

Week 11: Hardware Integration and Deployment​

Learning Outcomes​

  • Select appropriate hardware for robot
  • Deploy ROS 2/Isaac on hardware
  • Optimize for edge computing
  • Implement safety measures

Topics Covered​

  • Hardware selection criteria
  • Edge computing optimization
  • Real-time constraints
  • Safety implementation
  • Hardware debugging techniques

Assignments​

  • Select and justify hardware for robot
  • Deploy system on edge hardware
  • Optimize performance for real-time operation

Resources​

  • Hardware selection guides
  • Edge computing optimization
  • Safety implementation resources

Assessment​

  • Hardware selection report (10%)
  • Deployment implementation (15%)

Week 12: Capstone Project and Integration​

Learning Outcomes​

  • Integrate all components into complete system
  • Test system in real-world scenarios
  • Document and present project
  • Evaluate system performance

Topics Covered​

  • System integration challenges
  • Real-world testing procedures
  • Performance evaluation
  • Documentation and presentation
  • Future development planning

Assignments​

  • Complete integrated robot system
  • Conduct real-world testing
  • Prepare project documentation and presentation

Resources​

  • Integration best practices
  • Testing methodologies
  • Documentation templates

Assessment​

  • Integrated system demonstration (25%)
  • Project documentation (10%)
  • Final presentation (10%)

Assessment Breakdown​

Continuous Assessment (60%)​

  • Weekly assignments and quizzes: 30%
  • Mid-term project: 15%
  • Participation and engagement: 15%

Final Assessment (40%)​

  • Capstone project implementation: 25%
  • Final project documentation: 10%
  • Final presentation: 5%

Resources and Tools​

Software​

  • ROS 2 Humble Hawksbill
  • Gazebo Garden
  • NVIDIA Isaac ROS
  • Isaac Sim
  • Python 3.8+
  • Git for version control

Hardware (for practical work)​

  • NVIDIA Jetson AGX Orin or equivalent
  • RGB-D camera (Intel RealSense or equivalent)
  • LIDAR sensor (optional)
  • Robot platform (simulated or physical)

Learning Platforms​

  • Course textbook and documentation
  • Online ROS 2 tutorials
  • NVIDIA Isaac documentation
  • Research papers and publications

Support and Office Hours​

  • Instructor Office Hours: Tuesdays and Thursdays, 2-4 PM
  • TA Support: Monday, Wednesday, Friday, 10 AM-12 PM
  • Online Forum: Discussion and Q&A platform
  • Lab Access: Monday-Friday, 8 AM-8 PM

Accommodation and Accessibility​

All course materials are available in multiple formats:

  • Text-based documentation
  • Video tutorials with closed captions
  • Interactive simulations
  • Hands-on lab sessions

Students requiring special accommodations should contact the course instructor within the first week of class.

Try With AI​

Try asking your AI companion to help you plan your weekly study schedule based on this course structure, or ask for recommendations on which topics to focus on based on your background. You can also inquire about additional resources for any specific week or ask for guidance on managing the workload effectively.