AI for Robotics Training in Lille

This practical, instructor-led training in Lille focuses on rapid prototyping for robotics utilizing ROS 2 and Docker. Participants will acquire the ability to efficiently build, test, and deploy modular robotic systems, developing hands-on expertise in containerization, simulation, and version control to support scalable development.

This practical HRI course in Lille focuses on voice, gesture, and collaborative control. It guides learners in building intuitive, multimodal interfaces for secure human-robot collaboration, emphasizing hands-on design principles, speech recognition, gesture detection, and shared autonomy to boost usability and safety.

This practical training in Lille focuses on ROS-PLC integration and digital twins within industrial automation. Participants will learn to synchronize robotic systems with PLCs, create digital replicas for simulation purposes, and enhance production workflows through real-time data exchange and sophisticated monitoring methods.

Advanced training in Lille focusing on deep learning for robotic manipulation. Gain expertise in perception, grasp planning, and control using neural networks and ROS 2. Practical simulation exercises equip you to effectively deploy intelligent, adaptable grasping solutions on real robotic arms.

A specialized training program on Lille that focuses on the architecture and synchronization of multi-robot systems. Learners acquire the skills to model swarm intelligence, execute distributed decision-making, and enhance inter-agent collaboration through practical simulation in ROS 2 and Gazebo, targeting real-world implementation.

This instructor-led live training in Lille guides advanced professionals through the process of integrating TinyML into autonomous robotics. Learners will design optimized models, construct on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.

This 21-hour instructor-led training in Lille delves into safety cases, verification, and explainable AI within robotics. Participants will learn to implement hazard analysis, ethical governance, and compliance standards to foster transparent and trustworthy robotic decision-making in real-world settings.

This live training in Lille assists intermediate engineers in deploying TinyML models for robotics. You will learn to optimize inference for speed and energy efficiency, integrate AI into control systems, and create autonomous, low-latency robotic solutions directly on embedded hardware.

This instructor-led, live training in Lille (online or onsite) is aimed at intermediate-level participants who wish to explore the role of collaborative robots (cobots) and other human-centric AI systems in modern workplaces.

By the end of this training, participants will be able to:

• Understand the principles of Human-Centric Physical AI and its applications.
• Explore the role of collaborative robots in enhancing workplace productivity.
• Identify and address challenges in human-machine interactions.
• Design workflows that optimize collaboration between humans and AI-driven systems.
• Promote a culture of innovation and adaptability in AI-integrated workplaces.

This instructor-led live training in Lille explores the fundamentals of reinforcement learning and deep RL methodologies, including PPO and DDPG. Learners will build algorithms using Python and PyTorch, integrate them with ROS 2 and OpenAI Gym, and guide autonomous robots toward solving intricate tasks.

This instructor-led training on Lille focuses on applying computer vision to robotics through OpenCV and deep learning. Participants will master the implementation of perception pipelines, the integration of detection models, and the deployment of systems on embedded platforms for autonomous control.

This instructor-led, live training in Lille (online or onsite) targets advanced robotics engineers and AI researchers who wish to utilize Multimodal AI. The goal is to integrate various sensory data streams to create more autonomous and efficient robots capable of visual, auditory, and tactile interaction.

Upon completion, participants will be able to:

• Implement multimodal sensing within robotic systems.
• Develop AI algorithms for sensor fusion and decision-making.
• Build robots capable of executing complex tasks in dynamic environments.
• Address challenges related to real-time data processing and actuation.

This live, instructor-led training in Lille provides intermediate robotics engineers with practical proficiency in autonomous navigation and SLAM using ROS 2. Participants will learn to integrate LiDAR sensors, apply mapping algorithms, simulate scenarios in Gazebo, and deploy real-world navigation stacks on physical robots.

This live, instructor-led training in Lille (online or onsite) is designed for intermediate-level professionals aiming to elevate their capabilities in designing, programming, and deploying intelligent robotic systems for automation and beyond.

By the end of the course, participants will be able to:

• Comprehend the principles of Physical AI and its role in robotics and automation.
• Design and program intelligent robotic systems suited for dynamic environments.
• Apply AI models to enable autonomous decision-making in robots.
• Employ simulation tools for effective robotic testing and optimization.
• Tackle key challenges such as sensor fusion, real-time processing, and energy efficiency.

This instructor-led live training in Lille (online or onsite) is designed for engineers interested in learning how to apply artificial intelligence to mechatronic systems.

Upon completing this training, participants will be able to:

• Obtain a comprehensive overview of artificial intelligence, machine learning, and computational intelligence.
• Grasp the core concepts of neural networks and various learning methodologies.
• Select appropriate artificial intelligence approaches to address real-world challenges.
• Deploy AI solutions within mechatronic engineering contexts.