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 Duration 21 hours

Course Outline

Foundations of TinyML in Robotics

  • Understanding the key capabilities and constraints of TinyML
  • The role of edge AI in enhancing autonomous systems
  • Hardware considerations specific to mobile robots and drones

Embedded Hardware and Sensor Interfaces

  • Selecting microcontrollers and embedded boards suitable for robotics
  • Integrating cameras, IMUs, and proximity sensors
  • Balancing energy consumption and compute budgets

Data Engineering for Robotic Perception

  • Strategies for collecting and labeling data for robotics tasks
  • Techniques for signal and image preprocessing
  • Feature extraction methods suited for constrained devices

Model Development and Optimization

  • Choosing architectures for perception, detection, and classification
  • Building 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 Validation of TinyML-Driven Robots

  • Using simulation tools and field testing approaches
  • Evaluating performance metrics for embedded autonomy
  • Debugging and facilitating iterative improvement

Integration into Robotics Platforms

  • Deploying TinyML within ROS-based pipelines
  • Interfacing ML models with motor controllers
  • Maintaining reliability across varying hardware configurations

Summary and Next Steps

Requirements

  • A solid grasp of robotics system architectures
  • Practical experience with embedded development
  • Working knowledge of machine learning concepts

Target Audience

  • Robotics engineers
  • AI researchers
  • Embedded developers

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