Practical Rapid Prototyping for Robotics with ROS 2 & Docker Training Course

Artificial Intelligence (AI) for Robotics integrates machine learning, control systems, and sensor fusion to build intelligent machines capable of perceiving, reasoning, and acting autonomously. 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 aiming 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 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.

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.

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.

A bot, or chatbot, functions as a digital assistant designed to automate user interactions across various messaging platforms, enabling tasks to be completed more efficiently without requiring direct human contact.

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

Upon completion of this training, participants will be capable of:

• Understanding the diverse uses and applications of bots
• Gaining insight into the complete bot development process
• Exploring the various tools and platforms utilized for bot construction
• Constructing a sample chatbot for Facebook Messenger
• Building a sample chatbot using the Microsoft Bot Framework

Target Audience

• Developers interested in creating their own bot

Course Format

• A combination of lectures, discussions, exercises, and extensive hands-on practice

Edge AI empowers artificial intelligence models to operate directly on embedded or resource-limited devices. This approach significantly reduces latency and power consumption 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. The course focuses on implementing machine learning inference and optimization techniques directly on robotic hardware using TinyML and edge AI frameworks.

Upon completion of this training, participants will be capable of:

• Grasping the core principles of TinyML and edge AI applied to robotics.
• Converting and deploying AI models for on-device inference.
• Optimizing models to improve speed, reduce size, and enhance energy efficiency.
• Integrating edge AI systems into robotic control architectures.
• Evaluating performance and accuracy in real-world operational scenarios.

Course Format

• Interactive lectures and group discussions.
• Hands-on practice with TinyML and edge AI development toolchains.
• Practical exercises conducted on embedded and robotic hardware platforms.

Customization Options

• For organizations wishing to request a customized version of this course, please contact us to arrange the details.

This instructor-led, live training in Norway (online or onsite) is designed for intermediate-level participants eager to investigate the impact of collaborative robots (cobots) and other human-focused AI systems in contemporary work environments.

Upon completion of this training, participants will be equipped to:

• Grasp the core principles of Human-Centric Physical AI and its practical applications.
• Investigate how collaborative robots contribute to improved workplace productivity.
• Recognize and resolve challenges associated with human-machine interaction.
• Develop workflows that maximize collaboration between humans and AI-driven systems.
• Foster a culture of innovation and adaptability in workplaces utilizing AI.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical, hands-on course aimed at introducing participants to the design and implementation of intuitive interfaces for human–robot communication. This training blends theoretical foundations, design principles, and programming practice to help build natural and responsive interaction systems utilizing speech, gesture, and shared control techniques. Participants will gain skills in integrating perception modules, developing multimodal input systems, and designing robots capable of safe collaboration with humans.

Delivered as instructor-led live training, available either online or onsite, this program targets beginner to intermediate-level participants seeking to design and implement human–robot interaction systems that improve usability, safety, and overall user experience.

Upon completion of this training, participants will be able to:

• Grasp the foundational concepts and design principles of human–robot interaction.
• Create voice-based control and response mechanisms for robotic systems.
• Implement gesture recognition using computer vision techniques.
• Design collaborative control systems that ensure safe and shared autonomy.
• Evaluate HRI systems based on usability, safety standards, and human factors.

Format of the Course

• Interactive lectures and live demonstrations.
• Hands-on coding and design exercises.
• Practical experiments conducted 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 course designed to bridge the gap between industrial automation and contemporary robotics frameworks. Participants will gain the skills needed to integrate ROS-based robotic systems with PLCs for synchronized operations, while exploring digital twin environments to simulate, monitor, and optimize production processes. The curriculum emphasizes interoperability, real-time control, and predictive analysis utilizing digital replicas of physical systems.

This instructor-led, live training (available online or onsite) targets intermediate-level professionals aiming to develop practical competencies in connecting ROS-controlled robots with PLC environments and implementing digital twins to enhance automation and manufacturing efficiency.

Upon completing this training, participants will be able to:

• Comprehend the communication protocols facilitating interaction between ROS and PLC systems.
• Execute real-time data exchange between robots and industrial controllers.
• Create digital twins for monitoring, testing, and process simulation.
• Integrate sensors, actuators, and robotic manipulators into industrial workflows.
• Design and validate industrial automation systems using hybrid simulation environments.

Format of the Course

• Interactive lectures and architectural walkthroughs.
• Hands-on exercises focused on integrating ROS and PLC systems.
• Implementation of simulation and digital twin projects.

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 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.

The Multi-Robot Systems and Swarm Intelligence course is an advanced program designed to explore the design, coordination, and control of robotic teams, drawing inspiration from biological swarm behaviors. Participants will gain insights into modeling interactions, implementing distributed decision-making processes, and optimizing collaboration across multiple agents. By combining theoretical knowledge with practical simulation exercises, the course prepares learners for applications in logistics, defense, search and rescue operations, and autonomous exploration.

This instructor-led live training, available online or onsite, targets advanced-level professionals interested in designing, simulating, and implementing multi-robot and swarm-based systems using open-source frameworks and algorithms.

Upon completion of this training, participants will be able to:

• Grasp the principles and dynamics of swarm intelligence and cooperative robotics.
• Design effective communication and coordination strategies for multi-robot systems.
• Implement distributed decision-making and consensus algorithms.
• Simulate collective behaviors, including formation control, flocking, and coverage.
• Apply swarm-based techniques to real-world scenarios and optimization challenges.

Course Format

• Advanced lectures featuring in-depth algorithmic discussions.
• Practical coding and simulation exercises using ROS 2 and Gazebo.
• A collaborative project focused on applying swarm intelligence principles.

Customization Options

• For personalized training arrangements, please contact us.

This instructor-led, live training in Norway (online or onsite) is designed for advanced robotics engineers and AI researchers seeking to leverage Multimodal AI to integrate diverse sensory inputs. The objective is to build more autonomous and efficient robots that possess the ability to see, hear, and touch.

Upon completion of this training, participants will be able to:

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

This instructor-led, live training in Norway (online or onsite) is designed for intermediate-level participants who want to improve their capabilities in designing, programming, and deploying intelligent robotic systems for automation and other purposes.

Upon completing this training, participants will be able to:

• Understand the core 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.

Reinforcement learning (RL) constitutes a machine learning paradigm where autonomous agents acquire decision-making skills through continuous interaction with their environment. Within the field of robotics, RL empowers autonomous systems to cultivate adaptive control mechanisms and sophisticated decision-making capabilities by leveraging experiential learning and feedback loops.

This instructor-led live training, available both online and onsite, is designed for advanced machine learning engineers, robotics researchers, and developers aiming to design, implement, and deploy reinforcement learning algorithms specifically for robotic applications.

Upon completing this training, participants will be equipped to:

• Grasp the foundational principles and mathematical underpinnings of reinforcement learning.
• Implement key RL algorithms, including Q-learning, DDPG, and PPO.
• Integrate RL solutions with robotic simulation environments utilizing OpenAI Gym and ROS 2.
• Train robots to execute complex tasks autonomously via trial and error.
• Enhance training performance using deep learning frameworks such as PyTorch.

Course Format

• Interactive lectures and discussions.
• Practical implementation exercises using Python, PyTorch, and OpenAI Gym.
• Hands-on exercises within simulated or physical robotic environments.

Customization Options

• For customized training requests regarding this course, please contact us to arrange.

Smart Robotics involves embedding artificial intelligence into robotic systems to enhance perception, decision-making, and autonomous control capabilities.

This instructor-led live training, available both online and onsite, is designed for advanced robotics engineers, systems integrators, and automation leads who want to implement AI-driven perception, planning, and control within 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

• Interactive lecture and discussion.
• Lots 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.