Artificial Intelligence (AI) for Robotics Training Course

In this instructor-led, live training Sweden (online or onsite), participants will learn the technologies, frameworks, and techniques for programming various types of robots for use in nuclear technology and environmental systems.

The six-week course is conducted five days a week. Each day spans four hours and includes lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete real-world projects applicable to their work 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, along with 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 Sweden (online or on-site), participants will explore the various technologies, frameworks, and techniques for programming robots intended for nuclear technology and environmental systems.

The four-week course runs five days a week. Each day consists of four hours of instruction, including lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete real-world projects applicable to their work to practice their acquired knowledge.

The target hardware for this course will be simulated in 3D via simulation software. The code will then be loaded onto physical hardware (such as Arduino) for final deployment testing. The ROS (Robot Operating System) open-source framework, along with C++ and Python, will be used to program 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.

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.

Azure Bot Service integrates the capabilities of the Microsoft Bot Framework and Azure Functions, offering a robust platform for rapidly constructing intelligent bots.

During this instructor-led live training, participants will discover efficient methods for developing intelligent bots using Microsoft Azure.

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

Grasp the fundamental concepts underlying intelligent bots.

Construct intelligent bots utilizing cloud-based applications.

Acquire practical expertise in the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Implement established bot design patterns in real-world scenarios.

Create and deploy their first intelligent bot using Microsoft Azure.

Audience

This course is tailored for developers, hobbyists, engineers, and IT professionals with an interest in bot development.

Format of the course

The training blends lectures and discussions with exercises, placing a strong emphasis on hands-on practice.

OpenCV is an open-source library designed for computer vision, facilitating real-time image processing. Meanwhile, deep learning frameworks like TensorFlow supply the necessary tools for intelligent perception and decision-making capabilities within robotic systems.

This instructor-led live training, available both online and onsite, targets intermediate-level robotics engineers, computer vision specialists, and machine learning professionals who aim to leverage computer vision and deep learning techniques to enhance robotic perception and autonomy.

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

• Constructing computer vision pipelines using OpenCV.
• Incorporating deep learning models for object detection and recognition tasks.
• Leveraging vision-based data to guide robotic control and navigation.
• Merging classical vision algorithms with deep neural networks.
• Deploying computer vision solutions on embedded devices and robotic hardware.

Course Format

• Interactive lectures and group discussions.
• Practical exercises utilizing OpenCV and TensorFlow.
• Live laboratory implementation on either simulated or physical robotic platforms.

Customization Options

• For tailored training arrangements, please reach out to us to organize a customized session.

A bot, or chatbot, functions as a digital assistant designed to automate user interactions across various messaging platforms, enabling faster task completion without requiring direct human intervention.

During this instructor-led live training, participants will learn how to begin building bots by walking through the creation of sample chatbots using industry-standard development tools and frameworks.

By the conclusion of this training, participants will be capable of:

• Comprehending the diverse uses and applications of bots
• Gaining insight into the end-to-end bot development process
• Exploring the range of tools and platforms utilized in bot construction
• Developing a sample chatbot for Facebook Messenger
• Constructing a sample chatbot using the Microsoft Bot Framework

Audience

• Developers who wish to create their own bots

Course Format

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

Edge AI allows artificial intelligence models to operate directly on embedded or resource-limited devices, which minimizes latency and power usage while enhancing autonomy and privacy within robotic systems.

This instructor-led live training (available online or onsite) targets intermediate-level embedded developers and robotics engineers looking to apply machine learning inference and optimization techniques directly onto robotic hardware via TinyML and edge AI frameworks.

Upon completing this training, participants will be capable of:

• Gaining a solid understanding of TinyML and edge AI fundamentals for 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 scenarios.

Format of the Course

• Interactive lectures and discussions.
• Hands-on practice utilizing 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 Sweden (online or onsite) is designed for intermediate-level participants eager to investigate the role of collaborative robots (cobots) and other human-centric AI systems in contemporary workplaces.

Upon completing this training, participants will be able to:

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

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course aimed at introducing participants to the design and development of intuitive interfaces for human–robot communication. The training merges theoretical knowledge, design principles, and programming practice to create natural and responsive interaction systems leveraging speech, gesture, and shared control techniques. Participants will learn to integrate perception modules, build multimodal input systems, and design robots that safely collaborate with humans.

This instructor-led, live training (available online or onsite) targets beginner to intermediate participants who wish to design and implement human–robot interaction systems that improve usability, safety, and overall user experience.

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

• Grasp the fundamentals 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 course designed to bridge the gap between industrial automation and contemporary robotics frameworks. Participants will acquire the skills 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 places strong emphasis on 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) targets intermediate-level professionals seeking to develop practical competencies 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 linking ROS and PLC systems.
• Execute real-time data exchange between robots and industrial controllers.
• Create digital twins for monitoring, testing, and process simulation.
• Incorporate 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 architecture walkthroughs.
• Hands-on exercises focused on integrating ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Course Customization Options

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

This instructor-led, live training in Sweden (online or onsite) is designed for engineers who want to explore the application of artificial intelligence to mechatronic systems.

Upon completing this training, participants will be able to:

• Obtain an overview of artificial intelligence, machine learning, and computational intelligence.
• Comprehend the concepts of neural networks and various learning methodologies.
• Select appropriate artificial intelligence approaches for real-world problems.
• Implement AI applications within mechatronic engineering.

Multi-Robot Systems and Swarm Intelligence is an advanced training course that explores 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

• Advanced lectures with algorithmic deep dives.
• Hands-on coding and simulation in ROS 2 and Gazebo.
• Collaborative project applying swarm intelligence principles.

Course Customization Options

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

This instructor-led, live training in Sweden (online or onsite) is designed for advanced-level robotics engineers and AI researchers who wish to utilize Multimodal AI to integrate various sensory data, creating 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.

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.

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

This instructor-led training session, available both online and on-site, is designed for advanced robotics engineers, systems integrators, and automation specialists who aim to implement AI-driven perception, planning, and control within smart manufacturing settings.

Upon completing this training, participants will be able to:

• Comprehend and apply AI methodologies for robotic perception and sensor fusion.
• Create motion planning algorithms tailored for both collaborative and industrial robots.
• Implement learning-based control strategies to facilitate real-time decision-making.
• Seamlessly integrate intelligent robotic systems into smart factory workflows.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For bespoke training arrangements for this course, please get in touch with us.