Master AI algorithms for intelligent robotic systems
This in-depth training course aims to explore the most relevant artificial intelligence algorithms for robotic applications. It covers the basics of AI, machine learning techniques, computer vision, planning, and autonomous decision-making.
Is it for you ?
Robotics developers and engineers, innovation or digital transformation managers, technical trainers.
Prerequisites
Basic programming skills (Python recommended). Knowledge of mathematics (linear algebra, probability). Interest in robotics and AI technologies.
What You'll Walk Away With
- ✓ Understand robotic system architecture and the perception-decision-action loop
- ✓ Implement machine learning algorithms for robotic applications
- ✓ Develop computer vision solutions using OpenCV and TensorFlow
- ✓ Design navigation and path planning strategies (A*, Dijkstra)
- ✓ Simulate and test human-robot collaboration scenarios with advanced interactions
Training content
1 Fundamentals of AI and Intelligent Robotics
Chapter 1: Introduction to Artificial Intelligence
- Definitions and types (weak vs. strong AI)
- History and developments
- Areas of application
Chapter 2: Architecture of intelligent robotic systems
- Sensors, actuators, controllers
- Perception-decision-action loop
- Embedded systems and edge computing
Workshop 1: Simulation of a mobile robot with sensors
- Use of a simulator (e.g., Webots or Gazebo)
- Sensor configuration and data visualization
2 Learning algorithms and computer vision
Chapter 3: Machine learning applied to robotics
- Supervised vs. unsupervised learning
- Regression, classification, clustering
- Simple neural networks
Chapter 4: Computer vision and environment recognition
- Image processing and object detection
- Facial and gesture recognition algorithms
- SLAM (Simultaneous Localization and Mapping)
Workshop 2: Object detection with OpenCV and TensorFlow
- Implementation of a recognition model
- Application to a mobile robot
3 Planning, decision-making, and human-robot collaboration
Chapter 5: Planning and navigation algorithms
- Search algorithms (A, Dijkstra)
- Trajectory planning
- Obstacle avoidance
Chapter 6: Human-robot interaction and ethics
- Voice and gesture interfaces
- Reinforcement learning
- Ethical issues and safety
Workshop 3: Designing a collaborative human-robot scenario
- Setting up an assistant robot in a simulated environment
- Interaction with the user via voice or gesture commands
📌 Practical information
Our training sessions are offered in Montreal or Quebec City, in person or in a virtual classroom. Dates and locations are specified when you select your session below. If you have any questions, check out our FAQ.
