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Course Outline

Foundations of TinyML in Robotics

  • Core capabilities and inherent constraints of TinyML
  • The role of edge AI in autonomous systems
  • Hardware considerations for mobile robots and drones

Embedded Hardware and Sensor Interfaces

  • Microcontrollers and embedded boards suited for robotics
  • Integration of cameras, IMUs, and proximity sensors
  • Managing energy and compute budgets

Data Engineering for Robotic Perception

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

Model Development and Optimization

  • Selecting appropriate architectures for perception, detection, and classification
  • Building training pipelines for embedded ML
  • Model compression, quantization, and latency optimization

On-Device Perception and Control

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

Enhancing Autonomous Navigation

  • Implementing lightweight vision-based navigation
  • Obstacle detection and avoidance mechanisms
  • Maintaining environmental awareness under resource constraints

Testing and Validation of TinyML-Driven Robots

  • Utilizing simulation tools and field testing approaches
  • Defining performance metrics for embedded autonomy
  • Debugging strategies and iterative improvement

Integration into Robotics Platforms

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

Summary and Next Steps

Requirements

  • A solid understanding of robotics system architectures
  • Practical experience with embedded development
  • Familiarity with core machine learning concepts

Target Audience

  • Robotics engineers
  • AI researchers
  • Embedded developers
 21 Hours

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