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 Duration 21 hours

Course Outline

Foundations of TinyML in Robotics

  • Core capabilities and limitations of TinyML
  • The role of edge AI within autonomous systems
  • Hardware requirements for mobile robots and drones

Embedded Hardware and Sensor Integration

  • Microcontrollers and embedded boards suitable for robotics
  • Connecting cameras, IMUs, and proximity sensors
  • Managing energy and computational budgets

Data Engineering for Robotic Perception

  • Gathering and labeling data for specific robotics tasks
  • Techniques for signal and image preprocessing
  • Feature extraction methods for resource-constrained devices

Model Development and Optimization

  • Choosing architectures for perception, detection, and classification
  • Establishing training pipelines for embedded ML
  • Applying model compression, quantization, and latency optimization

On-Device Perception and Control

  • Executing inference on microcontrollers
  • Combining TinyML outputs with control algorithms
  • Ensuring real-time safety and responsiveness

Enhancing Autonomous Navigation

  • Implementing lightweight vision-based navigation
  • Detecting and avoiding obstacles
  • Maintaining environmental awareness under resource constraints

Testing and Validating TinyML-Driven Robots

  • Using simulation tools and field testing methods
  • Defining performance metrics for embedded autonomy
  • Debugging and continuous iterative improvement

Integration into Robotics Platforms

  • Deploying TinyML within ROS-based workflows
  • Connecting ML models with motor controllers
  • Ensuring reliability across different hardware configurations

Summary and Future Directions

Requirements

  • A solid grasp of robotics system architectures
  • Practical experience in embedded development
  • Knowledge of fundamental machine learning concepts

Target Audience

  • Robotics engineers
  • AI researchers
  • Embedded system developers

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