Computer Vision for Robotics: Perception with OpenCV & Deep Learning Training Course

The integration of Artificial Intelligence (AI) into robotics merges machine learning, control systems, and sensor fusion to develop intelligent machines that can perceive their surroundings, reason about them, and act autonomously. Leveraging contemporary tools such as ROS 2, TensorFlow, and OpenCV, engineers are empowered to create robots capable of intelligently navigating, planning, and interacting with complex real-world environments.

This instructor-led live training session, available both online and onsite, is designed for intermediate-level engineers aiming to develop, train, and deploy AI-driven robotic systems utilizing current 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.
• Deploy Kalman and Particle Filters for precise localization and tracking.
• Employ computer vision methods via OpenCV to enhance perception and object detection.
• Apply TensorFlow for motion prediction and learning-based control mechanisms.
• Integrate SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Create reinforcement learning models to refine robotic decision-making processes.

Course Format

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

Course Customization Options

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

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.

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

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

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

• Understand the various uses and applications of bots
• Grasp the complete bot development lifecycle
• 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 blend of lectures, discussions, exercises, and extensive hands-on practice

Edge AI allows artificial intelligence models to execute directly on embedded or resource-constrained devices, which reduces latency and power consumption while enhancing autonomy and privacy in robotic systems.

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

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

• Grasp the fundamentals of TinyML and edge AI for robotics.
• Convert and deploy AI models for on-device inference.
• Optimize models to improve speed, reduce size, and enhance energy efficiency.
• Integrate edge AI systems into robotic control architectures.
• Evaluate performance and accuracy in real-world scenarios.

Course Format

• Interactive lecture and discussion.
• Hands-on practice using 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 the Netherlands (online or onsite) is designed for intermediate-level participants eager to explore the role of collaborative robots (cobots) and other human-centric AI systems in contemporary workplaces.

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

• Grasp the core principles of Human-Centric Physical AI and its practical applications.
• Examine how collaborative robots contribute to enhancing workplace productivity.
• Identify and tackle challenges inherent in human-machine interactions.
• Design workflows that optimize collaboration between humans and AI-driven systems.
• Foster a culture of innovation and adaptability within AI-integrated workplaces.

The Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control programme is a practical course designed to familiarise participants with the design and implementation of intuitive interfaces for effective human–robot communication. This training integrates theory, design principles, and hands-on programming to create natural and responsive interaction systems utilizing speech, gestures, and shared control techniques. Participants will gain the skills to integrate perception modules, develop multimodal input systems, and design robots that collaborate safely with humans.

This instructor-led live training (available online or onsite) targets beginner to intermediate-level participants who wish 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 underpinning human–robot interaction.
• Develop voice-based control mechanisms and response systems for robots.
• Implement gesture recognition using computer vision techniques.
• Design collaborative control systems that ensure safe shared autonomy.
• Evaluate HRI systems against criteria of usability, safety, and human factors.

Course Format

• Interactive lectures accompanied by live demonstrations.
• Hands-on coding and design exercises.
• Practical experiments conducted in simulation or real robotic environments.

Course Customization Options

• To request a tailored training session for this course, please get in touch with us to make arrangements.

Industrial Robotics Automation: ROS-PLC Integration & Digital Twins is a practical course designed to bridge the gap between industrial automation and modern robotics frameworks. Participants will learn how to integrate ROS-based robotic systems with PLCs for synchronized operations, while exploring digital twin environments to simulate, monitor, and optimize production processes. The course emphasizes interoperability, real-time control, and predictive analysis using digital replicas of physical systems.

This instructor-led, live training (available online or onsite) targets intermediate-level professionals who wish to build practical skills in connecting ROS-controlled robots with PLC environments and implementing digital twins for automation and manufacturing optimization.

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

• Understand communication protocols between ROS and PLC systems.
• Implement real-time data exchange between robots and industrial controllers.
• Develop digital twins for monitoring, testing, and process simulation.
• Integrate sensors, actuators, and robotic manipulators within industrial workflows.
• Design and validate industrial automation systems using hybrid simulation environments.

Format of the Course

• Interactive lecture and architecture walkthroughs.
• Hands-on exercises integrating ROS and PLC systems.
• Simulation and digital twin project implementation.

Course Customization Options

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

This instructor-led, live training in the Netherlands (online or onsite) is designed for engineers seeking to understand the applicability of artificial intelligence to mechatronic systems.

Upon completing this training, participants will be capable of:

• Gaining a comprehensive overview of artificial intelligence, machine learning, and computational intelligence.
• Understanding the fundamental concepts of neural networks and various learning methodologies.
• Selecting the most effective artificial intelligence approaches for practical, real-world problems.
• Implementing AI applications within the field of mechatronic engineering.

Multi-Robot Systems and Swarm Intelligence is an advanced training course that delves into the design, coordination, and control of robotic teams inspired by biological swarm behaviors. Participants will acquire the skills to model interactions, implement distributed decision-making, and optimize collaboration across multiple agents. The curriculum integrates theoretical foundations 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 aiming 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.
• Develop 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 using 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 the Netherlands (online or onsite) is designed for advanced robotics engineers and AI researchers who wish to utilize Multimodal AI to integrate various sensory data. The aim is to create 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 processes.
• Create 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 the Netherlands (online or onsite) is designed for intermediate-level participants who want to improve their skills in designing, programming, and deploying intelligent robotic systems for automation and related fields.

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.

This hands-on course, 'Practical Rapid Prototyping for Robotics with ROS 2 & Docker', is designed to equip 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 creating modular robotic systems using Docker to ensure reproducibility and scalability. The curriculum highlights agility, version control, and collaborative practices ideal for innovation teams in the early stages of development.

This instructor-led live training (available online or onsite) targets beginner to intermediate participants looking to streamline their robotics development workflows through the use of ROS 2 and Docker.

Upon completion, participants will be able to:

• Establish a ROS 2 development environment within Docker containers.
• Create and test robotic prototypes in modular, reproducible setups.
• Utilize simulation tools to verify system performance prior to hardware deployment.
• Collaborate effectively via containerized robotics projects.
• Implement continuous integration and deployment principles in robotics pipelines.

Course Format

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

Customization Options

• For customized training requests, please contact us to arrange your session.

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.

Format of the Course

• 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.

This advanced course bridges the gap between robotic control and contemporary machine learning methodologies. Participants will investigate how deep learning can improve perception, motion planning, and dexterous grasping capabilities within robotic systems. By combining theoretical knowledge with simulation and practical coding assignments, the curriculum guides learners from perception-based control strategies toward end-to-end policy learning for complex manipulation tasks.

Delivered as an instructor-led live training session (available online or onsite), this program is designed for advanced professionals seeking to implement deep learning techniques to achieve intelligent, adaptable, and precise robotic manipulation.

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

• Construct perception models for object recognition and pose estimation.
• Train neural networks focused on grasp detection and motion planning.
• Integrate deep learning modules with robotic controllers utilizing ROS 2.
• Simulate and assess grasping and manipulation strategies within virtual environments.
• Deploy and optimize learned models onto actual or simulated robotic arms.

Course Format

• Expert-led lectures with in-depth algorithmic analysis.
• Practical coding and simulation exercises.
• Project-based implementation and testing phases.

Course Customization Options

• To request a customized version of this course, please contact us to make arrangements.

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

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

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

• Comprehend and apply AI techniques for robotic perception and sensor fusion.
• Create motion planning algorithms for both collaborative and industrial robots.
• Deploy learning-based control strategies to enable real-time decision making.
• Integrate intelligent robotic systems into smart factory workflows.

Course Format

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

Customization Options

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