AI for Robotics represents the meeting point between intelligence and motion — where algorithms think, sensors perceive, and machines act with purpose. It’s the frontier where data becomes dexterity, powering the next generation of autonomous systems, industrial robots, and intelligent machines.
In these instructor-led live training courses, participants explore how artificial intelligence transforms robotics into adaptive, learning systems. Through hands-on exercises, they dive into perception models, motion planning, reinforcement learning, and AI-driven control architectures that bring machines closer to human-like responsiveness.
Those joining online enter an environment that mirrors the pace of real labs — guided step by step through live demonstrations and collaborative coding via an interactive remote desktop. Every session unfolds as a shared exploration of logic and movement, not a one-way lecture.
For teams who prefer to build and test side by side, onsite live training in Nantes — held at customer premises or within NobleProg corporate training centers — transforms learning into experimentation. Robots, code, and imagination meet in a practical space where theory takes physical form.
Also known as Robotics AI or Intelligent Robotics, our training helps professionals bridge software and mechanics — building systems that sense, decide, and act with increasing autonomy and precision.
NobleProg — Your Local Training Provider
Nantes, Zenith
NobleProg Nantes, 4 rue Edith Piaf, Saint-Herblain, france, 44821
In the Parc d'Ar Mor zone, near the Zénith.
Car : from the ring road, Porte de Chézine Exit> Boulevard du Zenith > Esplanade Georges Brassens (restaurants) > Rue Edith Piaf on the right. From the N444 road (Nantes > Lorient), Exit #1 > boulevard Marcel Paul > Rue Edith Piaf at the right.
Parking Zénith P1 (free). Once parked, you can recognize the building: it's one of the tree bulding with zinc frontage.
Bicycle: free indoor parking
Public transport :
Tramway R1, Schoelcher station + 10 mn by foot through commercial center Atlantis
Tramway R1, François Mitterrand stop + bus 50, stop at Saulzaie station or bus 71, stop at the Zénith station
Tramway R3, Marcel Paul station + bus 50, Saulzaie station
Chronobus C6, Hermeland station+ bus 71, Zénith station
Bus : lignes 50 (Saulzaie station) or 71 (Zénith station)
Practical Rapid Prototyping for Robotics with ROS 2 & Docker is a hands-on course designed to help developers build, test, and deploy robotic applications efficiently. Participants will learn how to containerize robotics environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker for reproducibility and scalability. The course emphasizes agility, version control, and collaboration practices suitable for early-stage development and innovation teams.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level participants who wish to accelerate robotics development workflows using ROS 2 and Docker.
By the end of this training, participants will be able to:
Set up a ROS 2 development environment within Docker containers.
Develop and test robotic prototypes in modular, reproducible setups.
Use simulation tools to validate system behavior before hardware deployment.
Collaborate effectively using containerized robotics projects.
Apply continuous integration and deployment concepts in robotics pipelines.
Format of the Course also allows for the evaluation of participants.
Interactive lectures and demonstrations.
Hands-on exercises with ROS 2 and Docker environments.
Mini-projects focused on real-world robotic applications.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course designed to equip participants with the skills to design and implement intuitive interfaces for human–robot communication. This training blends theoretical concepts, design principles, and programming practice to create natural and responsive interaction systems utilizing speech, gesture, and shared control techniques. Participants will learn to integrate perception modules, develop multimodal input systems, and design robots that collaborate safely with humans.
This instructor-led, live training (available online or onsite) targets beginner to intermediate-level participants looking to design and implement human–robot interaction systems that enhance usability, safety, and user experience.
By the end of this training, participants will be able to:
Comprehend the foundations and design principles of human–robot interaction.
Develop voice-based control and response mechanisms for robots.
Implement gesture recognition using computer vision techniques.
Design collaborative control systems for safe and shared autonomy.
Evaluate HRI systems based on usability, safety, and human factors.
Format of the Course also allows for the evaluation of participants.
Interactive lectures and demonstrations.
Hands-on coding and design exercises.
Practical experiments in simulation or real robotic environments.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This practical course, "Industrial Robotics Automation: ROS-PLC Integration & Digital Twins," is designed to bridge the gap between industrial automation and contemporary robotics frameworks. Participants will learn how to synchronize robotic systems based on ROS (Robot Operating System) with PLCs (Programmable Logic Controllers) and utilize digital twin environments to simulate, monitor, and optimize production processes. The curriculum emphasizes interoperability, real-time control, and predictive analysis through the use of digital replicas of physical systems.
This instructor-led, live training, available online or onsite, is tailored for intermediate-level professionals seeking to develop practical skills in connecting ROS-controlled robots with PLC environments and implementing digital twins to enhance automation and manufacturing efficiency.
Upon completion of this training, participants will be able to:
Grasp the communication protocols facilitating interaction between ROS and PLC systems.
Establish real-time data exchange mechanisms between robots and industrial controllers.
Create digital twins for monitoring, testing, and simulating processes.
Seamlessly integrate sensors, actuators, and robotic manipulators into industrial workflows.
Design and validate industrial automation systems using hybrid simulation environments.
Course Format
Interactive lectures and architectural walkthroughs.
Practical exercises focused on integrating ROS and PLC systems.
Implementation of simulation and digital twin projects.
Customization Options
To request customized training for this course, please contact us to arrange your session.
Robot Manipulation and Grasping with Deep Learning is an advanced course that bridges robotic control with modern machine learning techniques. Participants will explore how deep learning can enhance perception, motion planning, and dexterous grasping in robotic systems. Through theory, simulation, and practical coding exercises, the course guides learners from perception-based control to end-to-end policy learning for manipulation tasks.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to apply deep learning methods to enable intelligent, adaptable, and precise robotic manipulation.
By the end of this training, participants will be able to:
Develop perception models for object recognition and pose estimation.
Train neural networks for grasp detection and motion planning.
Integrate deep learning modules with robotic controllers using ROS 2.
Simulate and evaluate grasping and manipulation strategies in virtual environments.
Deploy and optimize learned models on real or simulated robotic arms.
Format of the Course also allows for the evaluation of participants.
Expert-led lecture and algorithmic deep dives.
Hands-on coding and simulation exercises.
Project-based implementation and testing.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Multi-Robot Systems and Swarm Intelligence is an advanced training program that delves into the design, coordination, and control of robotic teams inspired by biological swarm behaviors. Participants will learn how to model interactions, implement distributed decision-making, and optimize collaboration across multiple agents. The course combines theory with hands-on simulation to prepare learners for applications in logistics, defense, search and rescue, and autonomous exploration.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to design, simulate, and implement multi-robot and swarm-based systems using open-source frameworks and algorithms.
By the end of this training, participants will be able to:
Understand the principles and dynamics of swarm intelligence and cooperative robotics.
Design communication and coordination strategies for multi-robot systems.
Implement distributed decision-making and consensus algorithms.
Simulate collective behaviors such as formation control, flocking, and coverage.
Apply swarm-based techniques to real-world scenarios and optimization problems.
Format of the Course also allows for the evaluation of participants.
Advanced lectures with algorithmic deep dives.
Hands-on coding and simulation in ROS 2 and Gazebo.
TinyML serves as a framework designed to deploy machine learning models on low-power microcontrollers and embedded platforms utilized within robotics and autonomous systems.
This instructor-led, live training session (available online or onsite) targets advanced-level professionals seeking to incorporate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.
Upon completing this course, participants will be equipped to:
Design optimized TinyML models tailored for robotics applications.
Implement on-device perception pipelines to enable real-time autonomy.
Integrate TinyML seamlessly into existing robotic control frameworks.
Deploy and test lightweight AI models on embedded hardware platforms.
Format of the Course also allows for the evaluation of participants.
Technical lectures complemented by interactive discussions.
Hands-on labs centered on embedded robotics tasks.
Practical exercises that simulate real-world autonomous workflows.
Course Customization Options
For organization-specific robotics environments, customization can be arranged upon request.
Safe and Explainable Robotics is a comprehensive training program focused on the safety, verification, and ethical governance of robotic systems. This course bridges theoretical knowledge with practical application by exploring safety argument methodologies, hazard analysis, and explainable AI approaches that render robotic decision-making transparent and trustworthy. Participants will learn how to ensure compliance, verify behaviors, and document safety assurance in accordance with international standards.
This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals who wish to apply verification, validation, and explainability principles to ensure the safe and ethical deployment of robotic systems.
By the end of this training, participants will be able to:
Develop and document safety arguments for robotic and autonomous systems.
Apply verification and validation techniques in simulation environments.
Understand explainable AI frameworks for robotics decision-making.
Integrate safety and ethics principles into system design and operation.
Communicate safety and transparency requirements to stakeholders.
Course Format
Interactive lecture and discussion.
Hands-on simulation and safety analysis exercises.
Case studies from real-world robotics applications.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Edge AI allows artificial intelligence models to operate directly on embedded or resource-limited devices, thereby reducing latency and power usage while enhancing autonomy and privacy within robotic systems.
This instructor-led live training (available online or onsite) is designed for intermediate-level embedded developers and robotics engineers seeking to implement machine learning inference and optimization techniques directly on robotic hardware using TinyML and edge AI frameworks.
Upon completion of this training, participants will be able to:
Grasp the core principles of TinyML and edge AI for robotics.
Convert and deploy AI models for on-device inference.
Optimize models to improve speed, reduce size, and enhance energy efficiency.
Integrate edge AI systems into robotic control architectures.
Evaluate performance and accuracy in real-world scenarios.
Course Format
Interactive lectures and discussions.
Hands-on practice utilizing TinyML and edge AI toolchains.
Practical exercises conducted on embedded and robotic hardware platforms.
Customization Options
To request customized training for this course, please contact us to arrange.
This instructor-led live training, available Nantes (online or onsite), targets intermediate-level participants interested in exploring the role of collaborative robots (cobots) and other human-centric AI systems in contemporary workplaces.
Upon completion of this training, participants will be able to:
Grasp the fundamental principles of Human-Centric Physical AI and their practical applications.
Examine how collaborative robots contribute to enhanced workplace productivity.
Recognize and resolve challenges associated with human-machine interactions.
Create workflows that maximize collaboration between humans and AI-driven systems.
Foster a culture of innovation and adaptability within AI-integrated workplaces.
Reinforcement learning (RL) is a machine learning approach where agents acquire decision-making skills by interacting with their environment. In the field of robotics, RL allows autonomous systems to develop adaptive control and decision-making abilities through experience and feedback.
This instructor-led, live training (available online or onsite) targets advanced-level machine learning engineers, robotics researchers, and developers who aim to design, implement, and deploy reinforcement learning algorithms for robotic applications.
Upon completion of this training, participants will be able to:
Grasp the principles and mathematics underlying reinforcement learning.
Implement RL algorithms, including Q-learning, DDPG, and PPO.
Integrate RL with robotic simulation environments using OpenAI Gym and ROS 2.
Train robots to autonomously execute complex tasks via trial and error.
Enhance training performance using deep learning frameworks such as PyTorch.
Course Format
Interactive lectures and discussions.
Practical implementation using Python, PyTorch, and OpenAI Gym.
Hands-on exercises in simulated or physical robotic environments.
Customization Options
For customized training on this course, please contact us to make arrangements.
OpenCV is an open-source computer vision library that enables real-time image processing, while deep learning frameworks such as TensorFlow provide the tools for intelligent perception and decision-making in robotic systems.
This instructor-led, live training (online or onsite) is aimed at intermediate-level robotics engineers, computer vision practitioners, and machine learning engineers who wish to apply computer vision and deep learning techniques for robotic perception and autonomy.
By the end of this training, participants will be able to:
Implement computer vision pipelines using OpenCV.
Integrate deep learning models for object detection and recognition.
Use vision-based data for robotic control and navigation.
Combine classical vision algorithms with deep neural networks.
Deploy computer vision systems on embedded and robotic platforms.
Format of the Course also allows for the evaluation of participants.
Interactive lecture and discussion.
Hands-on practice using OpenCV and TensorFlow.
Live-lab implementation on simulated or physical robotic systems.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in Nantes (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.
Smart Robotics involves integrating artificial intelligence into robotic systems to enhance perception, decision-making, and autonomous control capabilities.
This instructor-led live training, available online or onsite, is designed for advanced robotics engineers, systems integrators, and automation leads looking to implement AI-driven perception, planning, and control in smart manufacturing settings.
Upon completion of this training, participants will be able to:
Understand and apply AI techniques for robotic perception and sensor fusion.
Develop motion planning algorithms for collaborative and industrial robots.
Deploy learning-based control strategies for real-time decision making.
Integrate intelligent robotic systems into smart factory workflows.
Format of the Course also allows for the evaluation of participants.
Interactive lecture and discussion.
Plenty of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
ROS 2 (Robot Operating System 2) is an open-source framework intended to facilitate the creation of complex and scalable robotic applications.
This instructor-led live training, available online or on-site, targets intermediate robotics engineers and developers seeking to implement autonomous navigation and SLAM (Simultaneous Localization and Mapping) via ROS 2.
Upon completion of this training, participants will be capable of:
Configuring and setting up ROS 2 for autonomous navigation use cases.
Implementing SLAM algorithms for mapping and localization purposes.
Integrating sensors such as cameras and LiDAR with ROS 2.
Testing and simulating autonomous navigation within Gazebo.
Deploying navigation stacks onto physical robots.
Course Format
Interactive lectures and discussions.
Practical exercises utilizing ROS 2 tools and simulation environments.
Live laboratory implementation and testing on virtual or physical robots.
Course Customization Options
For requests regarding customized training for this course, please contact us to make arrangements.
This instructor-led live training in Nantes (online or onsite) is designed for intermediate-level participants aiming to refine their skills in designing, programming, and deploying intelligent robotic systems for automation and other applications.
Upon completion of this training, participants will be able to:
Grasp the fundamentals of Physical AI and its applications in robotics and automation.
Design and program intelligent robotic systems tailored for dynamic environments.
Implement AI models to enable autonomous decision-making in robots.
Utilize simulation tools for testing and optimizing robotic performance.
Tackle challenges such as sensor fusion, real-time processing, and energy efficiency.
Artificial Intelligence (AI) for Robotics merges machine learning, control systems, and sensor fusion to develop intelligent machines capable of autonomous perception, reasoning, and action. Leveraging modern tools such as ROS 2, TensorFlow, and OpenCV, engineers can now design robots that intelligently navigate, plan, and interact with real-world environments.
This instructor-led live training (available online or onsite) targets intermediate-level engineers looking to develop, train, and deploy AI-driven robotic systems using contemporary open-source technologies and frameworks.
Upon completion of this training, participants will be able to:
Utilize Python and ROS 2 to build and simulate robotic behaviors.
Implement Kalman and Particle Filters for localization and tracking purposes.
Apply computer vision techniques using OpenCV for perception and object detection.
Use TensorFlow for motion prediction and learning-based control.
Integrate SLAM (Simultaneous Localization and Mapping) for autonomous navigation.
Develop reinforcement learning models to enhance robotic decision-making.
Format of the Course also allows for the evaluation of participants.
Interactive lecture and discussion.
Hands-on implementation using ROS 2 and Python.
Practical exercises with simulated and real robotic environments.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training in Nantes (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
Understand the key concepts used in robotic technologies.
Understand and manage the interaction between software and hardware in a robotic system.
Understand and implement the software components that underpin robotics.
Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
Implement search algorithms and motion planning.
Implement PID controls to regulate a robot's movement within an environment.
Implement SLAM algorithms to enable a robot to map out an unknown environment.
Extend a robot's ability to perform complex tasks through Deep Learning.
Test and troubleshoot a robot in realistic scenarios.
Azure Bot Service integrates the Microsoft Bot Framework with Azure Functions, offering a robust platform for the rapid development of intelligent bots.
During this instructor-led live training, participants will discover efficient methods for developing intelligent bots using Microsoft Azure.
Upon completing the training, participants will be able to:
Grasp the fundamental concepts underlying intelligent bots.
A bot, or chatbot, functions as a digital assistant designed to automate user interactions across various messaging platforms. It enables tasks to be completed more efficiently, eliminating the need for direct human conversation.
In this instructor-led live training, participants will learn how to begin developing bots by creating sample chatbots using specialized development tools and frameworks.
Upon completing this training, participants will be able to:
Identify the various use cases and applications of bots
Grasp the entire bot development lifecycle
Examine the tools and platforms utilized in bot construction
Construct a sample chatbot for Facebook Messenger
Develop a sample chatbot using the Microsoft Bot Framework
Audience
Developers interested in creating their own bots
Course Format
A blend of lectures, discussions, exercises, and extensive hands-on practice
This instructor-led live training in Nantes (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.
A Smart Robot is an Artificial Intelligence (AI) system that can learn from its environment and its experience and build on its capabilities based on that knowledge. Smart Robots can collaborate with humans, working along-side them and learning from their behavior. Furthermore, they have the capacity for not only manual labor, but cognitive tasks as well. In addition to physical robots, Smart Robots can also be purely software based, residing in a computer as a software application with no moving parts or physical interaction with the world.
In this instructor-led, live training, participants will learn the different technologies, frameworks and techniques for programming different types of mechanical Smart Robots, then apply this knowledge to complete their own Smart Robot projects.
The course is divided into 4 sections, each consisting of three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section will conclude with a practical hands-on project to allow participants to practice and demonstrate their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
Understand the key concepts used in robotic technologies
Understand and manage the interaction between software and hardware in a robotic system
Understand and implement the software components that underpin Smart Robots
Build and operate a simulated mechanical Smart Robot that can see, sense, process, grasp, navigate, and interact with humans through voice
Extend a Smart Robot's ability to perform complex tasks through Deep Learning
Test and troubleshoot a Smart Robot in realistic scenarios
Audience
Developers
Engineers
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
To customize any part of this course (programming language, robot model, etc.) please contact us to arrange.
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Testimonials (2)
Supply of the materials (virtual machine) to get straight into the excersises, and the explanation of the Ros2 core. Why things work a certain way.
Arjan Bakema
Course - Autonomous Navigation & SLAM with ROS 2
its knowledge and utilization of AI for Robotics in the Future.
Ryle - PHILIPPINE MILITARY ACADEMY
Course - Artificial Intelligence (AI) for Robotics
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