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 Nijmegen — 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
Nijmegen
Fifty Two Degrees, Jonkerbosplein 52, Nijmegen, Netherlands, 6534 AB
Located in FiftyTwoDegrees, a striking black tower of 18 floors with a 'kink' in the top that is at an angle and known as an unrivaled business address in the east of the Netherlands. FiftyTwoDegrees is designed as a unique business, cultural and social center and is a combination of high concentration of knowledge companies and well thought-out offices that are grouped around a central square
Wageningen
Wageningen WUR, Stippeneng 2 , Wageningen, netherlands, 6708 WE
Wageningen is located on the banks of the Lower Rhine. Excavations date to the first settlements in this area around the Stone Age. The hills to the east of the city offered protection from floods of the Rhine from the Bronze Age. The city has a turbulent history and has been occupied or destroyed several times. The Rhine also changed course in 1421, moved further south and therefore had an adverse effect on the Wageningen trade. In the 17th century, the city started growing tobacco and there were several cigar manufacturers. The floodplains of the Rhine to the south also had several brickworks, one of which can still be seen.
In 1876, the Dutch government decided to build the first agricultural school in Wageningen because it was in the heart of the country and surrounded by a wide variety of soils. Since then, the city has grown enormously and Wageningen University is now a world-famous Life Sciences university. Wageningen also has an important inland port.
The halls of Impulse
Centrally located on Wageningen Campus, near the De halen van Impulse. The glass building and the cheerful colors provide a welcoming atmosphere. Impulse is an excellent location for not too large symposia or training courses. Impulse is located in building 115 on the Campus.
Address Stippeneng 2
6708 WE Wageningen
Wageningen Campus is indicated at the main roads of Wageningen. Follow the P-route to P3 on the campus. The route is indicated from all large parking spaces to the individual buildings on the campus. Impulse's building number is 115.
Arnhem
Arnhem Park Tower, Nieuwe stationsstraat 20, Arnhem, Netherlands, 6811 KS
The Arnhem Nijmegen region is known as a conference destination and has a thriving food and health sector, thanks in part to the presence of two universities.
The Park Tower is located in a modern building on top of the train station in the center of Arnhem's business district. The railway connects the city to national and international cities. Moreover, Arnhem is close to the German border and is easily accessible due to the many train routes. From the 13th and 14th floors of the city center you have a view over the city and the World Trade Center is right next door.
Edge AI enables artificial intelligence models to run directly on embedded or resource-constrained devices, reducing latency and power consumption while increasing autonomy and privacy in robotic systems.
This instructor-led, live training (online or onsite) is aimed at intermediate-level embedded developers and robotics engineers who wish to implement machine learning inference and optimization techniques directly on robotic hardware using TinyML and edge AI frameworks.
By the end of this training, participants will be able to:
Understand the fundamentals of TinyML and edge AI for robotics.
Convert and deploy AI models for on-device inference.
Optimize models for speed, size, and energy efficiency.
Integrate edge AI systems into robotic control architectures.
Evaluate performance and accuracy in real-world scenarios.
Format of the Course
Interactive lecture and discussion.
Hands-on practice using TinyML and edge AI toolchains.
Practical exercises on embedded and robotic hardware platforms.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Reinforcement learning (RL) is a machine learning paradigm where agents learn to make decisions by interacting with an environment. In robotics, RL enables autonomous systems to develop adaptive control and decision-making capabilities through experience and feedback.
This instructor-led, live training (online or onsite) is aimed at advanced-level machine learning engineers, robotics researchers, and developers who wish to design, implement, and deploy reinforcement learning algorithms in robotic applications.
By the end of this training, participants will be able to:
Understand the principles and mathematics of reinforcement learning.
Implement RL algorithms such as Q-learning, DDPG, and PPO.
Integrate RL with robotic simulation environments using OpenAI Gym and ROS 2.
Train robots to perform complex tasks autonomously through trial and error.
Optimize training performance using deep learning frameworks like PyTorch.
Format of the Course
Interactive lecture and discussion.
Hands-on implementation using Python, PyTorch, and OpenAI Gym.
Practical exercises in simulated or physical robotic environments.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
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
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.
ROS 2 (Robot Operating System 2) is an open-source framework designed to support the development of complex and scalable robotic applications.
This instructor-led, live training (online or onsite) is aimed at intermediate-level robotics engineers and developers who wish to implement autonomous navigation and SLAM (Simultaneous Localization and Mapping) using ROS 2.
By the end of this training, participants will be able to:
Set up and configure ROS 2 for autonomous navigation applications.
Implement SLAM algorithms for mapping and localization.
Integrate sensors such as LiDAR and cameras with ROS 2.
Simulate and test autonomous navigation in Gazebo.
Deploy navigation stacks on physical robots.
Format of the Course
Interactive lecture and discussion.
Hands-on practice using ROS 2 tools and simulation environments.
Live-lab implementation and testing on virtual or physical robots.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Artificial Intelligence (AI) for Robotics combines machine learning, control systems, and sensor fusion to create intelligent machines capable of perceiving, reasoning, and acting autonomously. Through modern tools like ROS 2, TensorFlow, and OpenCV, engineers can now design robots that navigate, plan, and interact with real-world environments intelligently.
This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers who wish to develop, train, and deploy AI-driven robotic systems using current open-source technologies and frameworks.
By the end of this training, participants will be able to:
Use Python and ROS 2 to build and simulate robotic behaviors.
Implement Kalman and Particle Filters for localization and tracking.
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 improve robotic decision-making.
Format of the Course
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 Nijmegen (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.
In this instructor-led, live training in Nijmegen (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 4-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 code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. 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.
Test and troubleshoot a robot in realistic scenarios.
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Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.
Ryle - PHILIPPINE MILITARY ACADEMY
Course - Artificial Intelligence (AI) for Robotics
Provisional Upcoming Courses (Contact Us For More Information)
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