AI for Robotics Training in Norway

Practical Rapid Prototyping for Robotics with ROS 2 & Docker is a hands-on course tailored to assist developers in efficiently building, testing, and deploying robotic applications. Participants will gain skills in containerizing robotics environments, integrating ROS 2 packages, and prototyping modular robotic systems using Docker, ensuring reproducibility and scalability. The course places a strong emphasis on agility, version control, and collaboration practices, making it ideal for early-stage development and innovation teams.

This instructor-led, live training (available both online and onsite) is designed for participants ranging from beginners to intermediate levels who are looking to streamline their 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 deploying hardware.
• Collaborate effectively on containerized robotics projects.
• Apply continuous integration and deployment concepts in robotics pipelines.

Format of the Course

• 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 introduce participants to the creation and implementation of intuitive interfaces for human-robot communication. The training blends theoretical knowledge with design principles and programming practices to develop natural and responsive interaction systems using speech, gestures, and shared control methods. Participants will learn how to integrate perception modules, create multimodal input systems, and design robots that can safely collaborate with humans.

This instructor-led, live training (online or onsite) is aimed at participants ranging from beginners to intermediate levels who wish 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:

• Grasp the foundational concepts 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

• 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.

Industrial Robotics Automation: ROS-PLC Integration & Digital Twins is a practical, hands-on course that focuses on integrating industrial automation with modern robotics frameworks. Participants will gain the skills to connect ROS-based robotic systems with PLCs for synchronized operations and explore digital twin environments to simulate, monitor, and optimize production processes. The course emphasizes interoperability, real-time control, and predictive analysis using digital replicas of physical systems.

This instructor-led, live training (online or onsite) is designed for intermediate-level professionals who wish to build practical skills in integrating ROS-controlled robots with PLC environments and implementing digital twins for automation and manufacturing optimization.

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

• Understand communication protocols between ROS and PLC systems.
• Implement real-time data exchange between robots and industrial controllers.
• Develop digital twins for monitoring, testing, and process simulation.
• Integrate sensors, actuators, and robotic manipulators within industrial workflows.
• Design and validate industrial automation systems using hybrid simulation environments.

Format of the Course

• Interactive lectures and architecture walkthroughs.
• Hands-on exercises integrating ROS and PLC systems.
• Simulation and digital twin project implementation.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Robot Manipulation and Grasping with Deep Learning is an advanced course that integrates robotic control with modern machine learning techniques. Participants will delve into how deep learning can improve perception, motion planning, and dexterous grasping in robotic systems. Through a combination of theoretical insights, simulation exercises, and practical coding sessions, the course takes learners from perception-based control to comprehensive end-to-end policy learning for manipulation tasks.

This instructor-led, live training (online or onsite) is designed for advanced professionals who aim to apply deep learning methods to achieve 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

• Expert-led lecture and detailed algorithmic discussions.
• 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 course that delves into the design, coordination, and control of robotic teams inspired by biological swarm behaviors. Participants will gain insights into modeling interactions, implementing distributed decision-making, and optimizing collaboration among multiple agents. The course blends theoretical knowledge with hands-on simulation to equip learners for applications in logistics, defense, search and rescue, and autonomous exploration.

This instructor-led, live training (available online or onsite) is tailored for advanced-level professionals who aim 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:

• Grasp the principles and dynamics of swarm intelligence and cooperative robotics.
• Develop 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 challenges.

Format of the Course

• Advanced lectures with in-depth algorithmic analysis.
• Hands-on coding and simulation using ROS 2 and Gazebo.
• A collaborative project applying swarm intelligence principles.

Course Customization Options

• For a customized training for this course, please contact us to arrange.

TinyML is a framework designed for deploying machine learning models on low-power microcontrollers and embedded platforms, which are commonly used in robotics and autonomous systems.

This instructor-led, live training (available both online and onsite) is tailored for advanced-level professionals who aim to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

Upon completing this course, participants will be able to:

• Design optimized TinyML models suitable for robotics applications.
• Implement on-device perception pipelines for real-time autonomy.
• Integrate TinyML into existing robotic control frameworks.
• Deploy and test lightweight AI models on embedded hardware platforms.

Format of the Course

• Technical lectures complemented by interactive discussions.
• Hands-on labs focusing on tasks related to embedded robotics.
• Practical exercises that simulate real-world autonomous workflows.

Course Customization Options

• For organization-specific robotics environments, customization can be arranged upon request.

Safe & Explainable Robotics is an extensive training program that focuses on the safety, verification, and ethical governance of robotic systems. The course bridges theoretical knowledge with practical application by delving into safety case methodologies, hazard analysis, and explainable AI techniques, which enhance the transparency and trustworthiness of robotic decision-making. Participants will learn how to ensure compliance, validate 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 aim to apply verification, validation, and explainability principles to guarantee the safe and ethical deployment of robotic systems.

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

• Develop and document safety cases for robotic and autonomous systems.
• Apply verification and validation techniques in simulation environments.
• Understand explainable AI frameworks used in robotics decision-making.
• Integrate safety and ethics principles into the design and operation of systems.
• Communicate safety and transparency requirements to stakeholders effectively.

Format of the Course

• Interactive lectures and discussions.
• 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 run directly on embedded or resource-limited devices, reducing latency and power consumption while enhancing autonomy and privacy in robotic systems.

This instructor-led, live training (online or onsite) is designed for intermediate-level embedded developers and robotics engineers who want 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:

• Grasp 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.

This instructor-led, live training in Norway (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.

Reinforcement learning (RL) is a machine learning approach where agents learn to make decisions by interacting with an environment. In the field of robotics, RL empowers autonomous systems to develop adaptive control and decision-making capabilities through experience and feedback.

This instructor-led, live training (online or onsite) is designed for advanced-level machine learning engineers, robotics researchers, and developers who want to design, implement, and deploy reinforcement learning algorithms in robotic applications.

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

• Grasp the principles and mathematical foundations 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.
• Enhance training performance using deep learning frameworks like PyTorch.

Format of the Course

• Interactive lectures and discussions.
• 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 facilitates real-time image processing, while deep learning frameworks like TensorFlow offer the tools needed for intelligent perception and decision-making in robotic systems.

This instructor-led, live training (online or onsite) is designed for intermediate-level robotics engineers, computer vision practitioners, and machine learning engineers who want to apply computer vision and deep learning techniques to enhance 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.
• Utilize 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 lectures and discussions.
• 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 Norway (online or onsite) is aimed at advanced-level robotics engineers and AI researchers who wish to utilize Multimodal AI for integrating various sensory data to create more autonomous and efficient robots that can see, hear, and touch.

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

• Implement multimodal sensing in robotic systems.
• Develop AI algorithms for sensor fusion and decision-making.
• Create robots that can perform complex tasks in dynamic environments.
• Address challenges in real-time data processing and actuation.

Smart Robotics involves the integration of artificial intelligence into robotic systems to enhance their perception, decision-making, and autonomous control.

This instructor-led, live training (available both online and on-site) is designed for advanced-level robotics engineers, systems integrators, and automation leaders who aim to implement AI-driven perception, planning, and control in smart manufacturing environments.

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

• Understand and apply AI techniques to improve robotic perception and sensor fusion.
• Develop motion planning algorithms for both collaborative and industrial robots.
• Implement learning-based control strategies for real-time decision-making.
• Integrate intelligent robotic systems into smart factory workflows.

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and practical activities.
• 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 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 applications in autonomous navigation.
• Implement SLAM algorithms for effective mapping and localization.
• Integrate sensors such as LiDAR and cameras into ROS 2 systems.
• Simulate and test autonomous navigation scenarios using Gazebo.
• Deploy navigation stacks on physical robotic platforms.

Format of the Course

• Interactive lectures and discussions.
• Practical exercises 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.

This instructor-led, live training in Norway (online or onsite) is aimed at intermediate-level participants who wish to enhance their skills in designing, programming, and deploying intelligent robotic systems for automation and beyond.

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

• Understand the principles of Physical AI and its applications in robotics and automation.
• Design and program intelligent robotic systems for dynamic environments.
• Implement AI models for autonomous decision-making in robots.
• Leverage simulation tools for robotic testing and optimization.
• Address challenges such as sensor fusion, real-time processing, and energy efficiency.

Artificial Intelligence (AI) for Robotics integrates machine learning, control systems, and sensor fusion to develop intelligent machines that can perceive, reason, and act independently. Utilizing modern tools such as ROS 2, TensorFlow, and OpenCV, engineers are now able to design robots capable of navigating, planning, and interacting with real-world environments in a sophisticated manner.

This instructor-led, live training (available online or onsite) is tailored for intermediate-level engineers who aim to develop, train, and deploy AI-driven robotic systems using the latest open-source technologies and frameworks.

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

• Use Python and ROS 2 to construct and simulate robotic behaviors.
• Implement Kalman and Particle Filters for localization and tracking.
• Apply computer vision techniques with OpenCV for perception and object detection.
• Utilize 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

• Interactive lecture and discussion.
• Hands-on implementation using ROS 2 and Python.
• Practical exercises in both 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 Norway (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 Norway (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.

The Azure Bot Service integrates the capabilities of the Microsoft Bot Framework and Azure Functions to facilitate the quick development of intelligent bots.

In this instructor-led, live training, participants will learn how to efficiently create an intelligent bot using Microsoft Azure.

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

• Understand the basics of intelligent bots
• Learn how to develop intelligent bots using cloud applications
• Grasp the use of the Microsoft Bot Framework, the Bot Builder SDK, and the Azure Bot Service
• Comprehend how to design bots using bot patterns
• Create their first intelligent bot using Microsoft Azure

Audience

• Developers
• Hobbyists
• Engineers
• IT Professionals

Format of the course

• Combination of lecture, discussion, exercises, and extensive hands-on practice

A chatbot or bot acts as a digital assistant that automates user interactions on various messaging platforms, enabling tasks to be completed more efficiently without the need for human-to-human communication.

In this instructor-led, live training, participants will learn how to start developing a bot by working through the creation of sample chatbots using specialized development tools and frameworks.

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

• Comprehend the diverse uses and applications of bots
• Understand the entire process involved in bot development
• Explore the various tools and platforms used for building bots
• Create a sample chatbot for Facebook Messenger
• Develop a sample chatbot using Microsoft Bot Framework

Audience

• Developers interested in creating their own bot

Format of the course

• Part lecture, part discussion, exercises, and extensive hands-on practice

This instructor-led, live training in Norway (online or onsite) is aimed at engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems.

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

• Gain an overview of artificial intelligence, machine learning, and computational intelligence.
• Understand the concepts of neural networks and different learning methods.
• Choose artificial intelligence approaches effectively for real-life problems.
• Implement AI applications in mechatronic engineering.

A Smart Robot is an Artificial Intelligence (AI) system capable of learning from its surroundings and experiences, enhancing its capabilities based on the acquired knowledge. These robots can collaborate with humans, working alongside them and adapting to their behaviors. In addition to performing manual tasks, they are also equipped to handle cognitive functions. Beyond physical robots, Smart Robots can exist purely as software applications, residing in a computer without any physical components or interactions.

In this instructor-led, live training, participants will explore various technologies, frameworks, and techniques for programming different types of mechanical Smart Robots. They will then apply this knowledge to complete their own Smart Robot projects.

The course is structured into four sections, each comprising three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section concludes with a practical, hands-on project to allow participants to practice and demonstrate their newly acquired skills.

For this course, the target hardware will be simulated in 3D using simulation software. The ROS (Robot Operating System) open-source framework, along with C++ and Python, will be utilized for programming the robots.

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

• Understand the fundamental concepts used in robotic technologies
• Manage the interaction between software and hardware in a robotic system
• Implement the software components that drive Smart Robots
• Build and operate a simulated mechanical Smart Robot capable of seeing, sensing, processing, grasping, navigating, and interacting with humans through voice commands
• Enhance a Smart Robot's ability to perform complex tasks using Deep Learning techniques
• Test and troubleshoot a Smart Robot in realistic scenarios

Audience

• Developers
• Engineers

Format of the course

• Part lecture, part discussion, exercises, and extensive hands-on practice

Note

• To customize any aspect of this course (programming language, robot model, etc.), please contact us to arrange.