TinyML for Autonomous Systems and Robotics Training Course

Artificial Intelligence (AI) for Robotics merges machine learning, control systems, and sensor fusion to develop intelligent machines capable of autonomous perception, reasoning, and action. Leveraging modern tools such as ROS 2, TensorFlow, and OpenCV, engineers can now design robots that intelligently navigate, plan, and interact within real-world environments.

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

Upon completing this training, participants will be equipped to:

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

Course Format

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

Course Customization Options

For customized training requests related to this course, please reach out to us to make arrangements.

ROS 2 (Robot Operating System 2) serves as an open-source framework tailored for developing complex and scalable robotic applications.

This instructor-led live training, available both online and onsite, targets robotics engineers and developers at an intermediate level who aim to implement autonomous navigation and SLAM (Simultaneous Localization and Mapping) using ROS 2.

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

• Configure and set up ROS 2 for autonomous navigation use cases.
• Deploy SLAM algorithms to achieve mapping and localization.
• Integrate hardware sensors, including LiDAR and cameras, with ROS 2.
• Simulate and validate autonomous navigation scenarios within Gazebo.
• Deploy navigation stacks onto physical robotic platforms.

Course Format

• Interactive lectures and group discussions.
• Practical exercises utilizing ROS 2 tools and simulation environments.
• Live laboratory implementation and testing on either virtual or physical robots.

Customization Options

• For inquiries regarding customized training tailored to this course, please reach out to us to arrange your schedule.

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 faster task completion without requiring direct communication with a human representative.

Through this instructor-led live training, participants will gain practical insights into bot development by constructing sample chatbots using established development tools and frameworks.

Upon completing this training, participants will be capable of:

• Identifying the diverse applications and use cases of bots
• Grasping the end-to-end bot development lifecycle
• Examining the various tools and platforms utilized in bot construction
• Constructing a sample chatbot for Facebook Messenger
• Developing a sample chatbot using the Microsoft Bot Framework

Target Audience

• Developers looking to build their own bots

Course Format

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

Edge AI allows artificial intelligence models to operate directly on embedded or resource-constrained devices, thereby lowering latency and power usage while boosting autonomy and privacy in robotic applications.

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

Upon completing this training, participants will be able to:

• Grasp the core concepts of TinyML and edge AI within the context of robotics.
• Transform and deploy AI models for on-device inference.
• Enhance models regarding speed, size, and energy efficiency.
• Incorporate edge AI systems into robotic control architectures.
• Assess performance and accuracy in real-world conditions.

Course Format

• Interactive lectures and discussions.
• Practical exercises using TinyML and edge AI toolchains.
• Hands-on work on embedded and robotic hardware platforms.

Customization Options

• For customized training arrangements, please contact us.

This instructor-led, live training in Czech Republic (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.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course designed to introduce participants to the design and implementation of intuitive interfaces for human–robot communication. The training combines theory, design principles, and programming practice to build natural and responsive interaction systems using speech, gesture, and shared control techniques. Participants will learn how to integrate perception modules, develop multimodal input systems, and design robots that safely collaborate with humans.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level participants 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:

• Understand the foundations 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: Integrating ROS with PLCs and Digital Twins is a practical course designed to bridge the gap between traditional industrial automation and modern robotics frameworks. Participants will gain the skills needed to integrate ROS-based robotic systems with PLCs for synchronized operations, while also exploring digital twin environments to simulate, monitor, and optimize production processes. The course places a strong emphasis on interoperability, real-time control, and predictive analysis by leveraging digital replicas of physical systems.

This instructor-led live training is available both online and onsite, targeting intermediate-level professionals who want 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 able to:

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

Course Format

• Interactive lectures accompanied by architecture walkthroughs.
• Practical exercises focused on integrating ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Customization Options

This instructor-led live training in Czech Republic (online or onsite) targets engineers interested in learning how artificial intelligence applies to mechatronic systems.

By the conclusion 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 the ability to model interactions, implement distributed decision-making processes, and optimize collaboration among multiple agents. This course blends theoretical foundations with practical simulations, preparing learners for applications in logistics, defense, search and rescue, and autonomous exploration.

This instructor-led live training is available both online and onsite. It is targeted at advanced-level professionals looking to design, simulate, and implement multi-robot and swarm-based systems using open-source frameworks and algorithms.

Upon completing 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 deep dives into algorithms.
• Hands-on coding and simulation exercises using ROS 2 and Gazebo.
• A collaborative project focused on applying swarm intelligence principles.

Course Customization Options

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

This instructor-led, live training in Czech Republic (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 that can see, hear, and touch.

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.
• Build robots capable of executing complex tasks in dynamic environments.
• Overcome challenges related to real-time data processing and actuation.

This instructor-led, live training in Czech Republic (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 practical, hands-on course designed to enable developers to build, test, and deploy robotic applications with greater efficiency. Participants will learn how to containerize robotic environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker to ensure reproducibility and scalability. The course places a strong emphasis on agility, version control, and collaborative practices that are well-suited for early-stage development and innovation teams.

This instructor-led, live training (available online or onsite) is intended for beginner to intermediate-level participants who want to accelerate their robotics development workflows using ROS 2 and Docker.

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

• Establish a ROS 2 development environment within Docker containers.
• Develop and test robotic prototypes in modular, reproducible setups.
• Utilize simulation tools to validate system behavior prior to hardware deployment.
• Collaborate effectively through containerized robotics projects.
• Apply continuous integration and deployment concepts within robotics pipelines.

Course Format

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

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.

Reinforcement Learning (RL) represents a machine learning approach in which agents acquire decision-making skills by engaging with their surroundings. Within the field of robotics, RL empowers autonomous systems to build adaptive control mechanisms and decision-making competencies through experiential learning and feedback loops.

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

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

• Grasp the core principles and mathematical foundations of reinforcement learning.
• Implement RL algorithms including Q-learning, DDPG, and PPO.
• Integrate RL 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 practice in simulated or physical robotic settings.

Customization Options

• For customized training arrangements, please contact us directly.

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

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

Upon completing this training, participants will be able to:

• Understand and apply AI techniques for robotic perception and sensor fusion.
• Develop motion planning algorithms for both 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 lectures and discussions.
• Numerous exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.