Physical AI for Robotics and Automation Training Course

AI for Robotics integrates machine learning, control systems, and sensor fusion to develop intelligent machines that can perceive, reason, and act autonomously. Leveraging modern tools such as ROS 2, TensorFlow, and OpenCV, engineers can design robots that navigate, plan, and interact with real-world environments in an intelligent manner.

This instructor-led, live training (available online or onsite) is designed for intermediate-level engineers looking to develop, train, and deploy AI-driven robotic systems using current 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.
• 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.

Course Format

• Interactive lectures and discussions.
• Practical implementation using ROS 2 and Python.
• Hands-on exercises with both simulated and real robotic environments.

Customization Options

For requests regarding customized training for this course, please contact us to make arrangements.

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.

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 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.

This hands-on course on Practical Rapid Prototyping for Robotics with ROS 2 & Docker equips developers with the skills to efficiently build, test, and deploy robotic applications. Participants will gain expertise in containerizing robotics environments, integrating ROS 2 packages, and prototyping modular robotic systems using Docker to ensure reproducibility and scalability. The curriculum emphasizes agility, version control, and collaborative practices ideal for early-stage development and innovation teams.

Delivered as live, instructor-led training (available online or onsite), this program targets beginner to intermediate-level participants looking to streamline robotics development workflows using ROS 2 and Docker.

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

• Establishing a ROS 2 development environment within Docker containers.
• Developing and testing robotic prototypes within modular, reproducible setups.
• Utilizing simulation tools to validate system behavior prior to hardware deployment.
• Collaborating effectively through containerized robotics projects.
• Implementing continuous integration and deployment concepts within robotics pipelines.

Course Format

• Interactive lectures and live demonstrations.
• Practical exercises involving ROS 2 and Docker environments.
• Mini-projects focused on real-world robotic applications.

Course Customization Options

• For customized training options, please reach out to us to arrange your specific requirements.

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.

Course Format

• 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.

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.