TinyML for Autonomous Systems and Robotics Training Course

The field of Artificial Intelligence (AI) in Robotics merges machine learning, control systems, and sensor fusion to develop intelligent machines that can perceive, reason, and act autonomously. By leveraging contemporary tools such as ROS 2, TensorFlow, and OpenCV, engineers are now empowered to create robots capable of intelligent navigation, planning, and interaction within real-world settings.

This instructor-led live training, available online or onsite, is designed for intermediate-level engineers looking to develop, train, and deploy AI-powered robotic systems using modern open-source technologies and frameworks.

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

• Utilize Python and ROS 2 to construct and simulate robotic behaviors.
• Apply Kalman and Particle Filters for precise localization and tracking.
• Employ computer vision techniques via OpenCV for perception and object detection.
• Leverage TensorFlow for motion prediction and learning-based control.
• Integrate SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Develop reinforcement learning models to enhance robotic decision-making processes.

Course Format

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

Customization Options

To arrange a customized training session for this course, please get in touch with us.

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 for computer vision that facilitates real-time image processing, while deep learning frameworks like TensorFlow offer the necessary tools for enabling intelligent perception and decision-making capabilities within robotic systems.

This instructor-led live training, available online or on-site, is designed for intermediate-level robotics engineers, computer vision specialists, and machine learning engineers aiming to leverage computer vision and deep learning techniques for robotic perception and autonomy.

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

• Building computer vision pipelines using OpenCV.
• Integrating deep learning models for object detection and recognition.
• Leveraging vision-based data for robotic control and navigation.
• Merging classical vision algorithms with deep neural networks.
• Deploying computer vision solutions on embedded and robotic platforms.

Course Format

• Interactive lectures and discussions.
• Practical exercises using OpenCV and TensorFlow.
• Live laboratory implementation on simulated or physical robotic systems.

Customization Options

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

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.

Through this instructor-led live training, participants will learn how to begin developing bots by building sample chatbots using dedicated bot development tools and frameworks.

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

• Understand the various uses and applications of bots
• Comprehend the complete bot development process
• Explore the different tools and platforms utilized in bot construction
• Construct a sample chatbot for Facebook Messenger
• Construct a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bot

Format of the course

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

Edge AI empowers artificial intelligence models to operate directly on embedded or resource-constrained devices, thereby minimizing latency and power usage while enhancing the autonomy and privacy of robotic systems.

This instructor-led training, available online or onsite, is designed for intermediate-level embedded developers and robotics engineers aiming to implement machine learning inference and optimization techniques directly on robotic hardware via TinyML and edge AI frameworks.

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

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

Course Format

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

Customization Options

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

This instructor-led, live training in Greece (offered online or onsite) is designed for intermediate-level learners interested in exploring the role of collaborative robots (cobots) and other human-centric AI systems in modern workplaces.

Upon completing this training, participants will be able to:

• Understand the core principles of Human-Centric Physical AI and their 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.

The course 'Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control' is a practical programme aimed at equipping participants with the skills to design and implement intuitive interfaces for effective human–robot communication. This training blends theoretical knowledge, design principles, and hands-on programming to create natural and responsive interaction systems utilising speech, gestures, and shared control methods. Participants will gain expertise in integrating perception modules, developing multimodal input systems, and designing robots that collaborate safely with humans.

Delivered by an instructor, this live training (available online or onsite) is tailored for beginners to intermediate-level professionals looking to design and implement human–robot interaction systems that improve usability, safety, and overall user experience.

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

• Grasping the foundational concepts and design principles of human–robot interaction.
• Creating voice-based control and response mechanisms for robotic systems.
• Implementing gesture recognition through computer vision techniques.
• Designing collaborative control systems that ensure safe and shared autonomy.
• Assessing HRI systems against criteria of usability, safety, and human factors.

Course Format

• Interactive lectures and live demonstrations.
• Practical coding and design exercises.
• Hands-on experimentation within simulation or real-world robotic environments.

Customisation Options for the Course

• For a bespoke training version of this course, please contact us to arrange your session.

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 emphasizes 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 aiming to develop practical expertise 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 equipped to:

• Comprehend the communication protocols facilitating interaction between ROS and PLC systems.
• Implement real-time data exchange mechanisms between robots and industrial controllers.
• Develop digital twins for monitoring, testing, and process simulation purposes.
• 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 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 Greece (available online or onsite) is designed for engineers who wish to explore the application 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.

Multi-Robot Systems and Swarm Intelligence is an advanced training course that delves into the design, coordination, and control of robotic teams, drawing inspiration from biological swarm behaviors. Participants will acquire skills in modeling interactions, implementing distributed decision-making, and optimizing collaboration across multiple agents. This course integrates theoretical knowledge with practical simulation exercises, preparing learners for applications in logistics, defense, search and rescue, and autonomous exploration.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals eager to design, simulate, and implement 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 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 featuring algorithmic deep dives.
• Hands-on coding and simulation exercises in ROS 2 and Gazebo.
• A 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 Greece (online or onsite) targets advanced robotics engineers and AI researchers looking to leverage Multimodal AI. The course focuses on integrating diverse sensory data to build more autonomous and efficient robots capable of seeing, hearing, and touching.

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

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

This instructor-led live training in Greece (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.

Practical Rapid Prototyping for Robotics with ROS 2 & Docker is a hands-on course designed to help developers build, test, and deploy robotic applications efficiently. Participants will learn how to containerize robotics environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker for reproducibility and scalability. The course emphasizes agility, version control, and collaboration practices suitable for early-stage development and innovation teams.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level participants who wish to accelerate 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 hardware deployment.
• Collaborate effectively using 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.

Reinforcement learning (RL) constitutes a machine learning paradigm in which agents acquire decision-making capabilities through interaction with their environment. Within the field of robotics, RL empowers autonomous systems to cultivate adaptive control and decision-making skills derived from experience and feedback.

This instructor-led training session, available either online or on-site, targets advanced machine learning engineers, robotics researchers, and developers who intend to design, implement, and deploy reinforcement learning algorithms for robotic applications.

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

• Grasp the fundamental principles and mathematical foundations of reinforcement learning.
• Implement RL algorithms, including Q-learning, DDPG, and PPO.
• Integrate RL with robotic simulation environments via OpenAI Gym and ROS 2.
• Enable robots to execute complex tasks autonomously through trial and error.
• Enhance training performance by leveraging deep learning frameworks such as PyTorch.

Course Format

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

Customization Options

• To arrange customized training for this course, please get in touch with us.

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

This instructor-led live training, available either online or onsite, is designed for advanced robotics engineers, systems integrators, and automation leaders who aim to implement AI-driven perception, planning, and control within smart manufacturing settings.

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

• Comprehend and apply AI methodologies for robotic perception and sensor fusion.
• Create motion planning algorithms for both collaborative and industrial robots.
• Implement learning-based control strategies to enable 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-lab environment.

Customization Options for the Course

• To arrange a tailored training session for this course, please get in touch with us.