Practical Rapid Prototyping for Robotics with ROS 2 & Docker Training Course

The integration of Artificial Intelligence (AI) with robotics merges machine learning, control systems, and sensor fusion to engineer intelligent machines that can independently perceive, reason about their surroundings, and act. Leveraging contemporary tools such as ROS 2, TensorFlow, and OpenCV, engineers are empowered to design robots capable of navigating, planning paths, and interacting with real-world environments in an intelligent manner.

This instructor-led live training, available both online and on-site, is designed for intermediate-level engineers seeking to develop, train, and deploy AI-powered 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 construct and simulate robotic behaviors.
• Implement Kalman and Particle Filters to enhance localization and tracking accuracy.
• Apply computer vision techniques via OpenCV for perception and object detection tasks.
• Employ TensorFlow for motion prediction and learning-based control strategies.
• Integrate SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Develop reinforcement learning models to refine robotic decision-making processes.

Course Format

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

Course Customization Options

To request customized training for this course, please contact us to arrange your needs.

This instructor-led live training, available online or on-site, equips participants with the technologies, frameworks, and techniques needed to program robots for applications in nuclear technology and environmental systems.

The 6-week course runs five days a week. Each day consists of four hours of lectures, discussions, and hands-on robot development in a live lab environment. Participants will engage in real-world projects applicable to their work to practice their acquired skills.

The target hardware is simulated in 3D using simulation software. The ROS (Robot Operating System) open-source framework, along with C++ and Python, is used for robot programming.

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 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 (offered online or onsite) targets intermediate-level robotics engineers, computer vision specialists, and machine learning professionals who want to leverage computer vision and deep learning techniques for robotic perception and autonomy.

Upon completing this training, participants will be able to:

• Build computer vision pipelines using OpenCV.
• Incorporate deep learning models for object detection and recognition.
• Leverage vision-based data to guide robotic control and navigation.
• Merge classical vision algorithms with deep neural networks.
• Deploy computer vision solutions on embedded and robotic platforms.

Course Format

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

Customization Options

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

A bot or chatbot acts as a virtual assistant, automating user interactions across various messaging platforms to accelerate tasks without requiring direct contact with a human operator.

In this instructor-led live training, participants will learn the fundamentals of bot development by creating sample chatbots using industry-standard development tools and frameworks.

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

• Understand the diverse uses and applications of bots
• Grasp the end-to-end process of developing bots
• Explore the various tools and platforms utilized in bot construction
• Construct a sample chatbot for Facebook Messenger
• Develop a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bots

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Edge AI allows artificial intelligence models to execute directly on embedded or resource-constrained devices, which helps reduce latency and power usage while enhancing the autonomy and privacy of robotic systems.

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

Upon completing this training, participants will be able to:

• Grasp the core concepts of edge AI and TinyML 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 applications.

Course Format

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

Customization Options

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

This instructor-led live training in Vietnam (online or onsite) is tailored for intermediate-level participants aiming to investigate the role of collaborative robots (cobots) and other human-centric AI systems in modern work environments.

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 aimed at introducing participants to the design and implementation of intuitive interfaces for human–robot communication. This training merges theoretical knowledge, design principles, and programming practice to create natural and responsive interaction systems using speech, gestures, and shared control methods. Participants will learn how to integrate perception modules, develop multimodal input systems, and design robots that can safely collaborate with humans.

Delivered by an instructor via live online or onsite sessions, this course targets beginner to intermediate-level participants who wish to design and implement human–robot interaction systems that enhance usability, safety, and user experience.

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

• Grasp the foundational concepts 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 and PLCs with Digital Twins is a practical, hands-on program designed to connect industrial automation technologies with contemporary robotics frameworks. Participants will learn how to link ROS-based robotic systems with Programmable Logic Controllers (PLCs) to achieve synchronized operations, while also exploring digital twin environments to simulate, monitor, and enhance production workflows. The curriculum focuses on system interoperability, real-time control mechanisms, and predictive analysis utilizing digital replicas of physical assets.

This live, instructor-led training—available either online or onsite—is tailored for intermediate-level professionals seeking to develop practical expertise in linking ROS-controlled robots with PLC environments and implementing digital twins to optimize automation and manufacturing processes.

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

• Grasping the communication protocols used between ROS and PLC systems.
• Executing real-time data exchange between robotic units and industrial controllers.
• Creating digital twins for monitoring, testing, and process simulation.
• Integrating sensors, actuators, and robotic manipulators into industrial workflows.
• Designing and validating industrial automation systems through hybrid simulation environments.

Course Format

• Interactive lectures combined with architectural walkthroughs.
• Practical exercises involving the integration of ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Customization Options

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

This instructor-led live training in Vietnam (online or on-site) is designed for engineers who wish to learn about the applicability 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 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.

Course Format

• 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 Vietnam (online or onsite) targets advanced robotics engineers and AI researchers aiming 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, 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.
• Tackle challenges related to real-time data processing and actuation.

This live, instructor-led training in Vietnam (online or onsite) is designed for intermediate-level professionals seeking to improve 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:

• Grasp the core concepts of Physical AI and its practical applications in robotics and automation.
• Design and code intelligent robotic systems suitable for dynamic environments.
• Apply AI models to enable autonomous decision-making in robots.
• Utilize simulation tools for testing and optimizing robot performance.
• Tackle common challenges such as sensor fusion, real-time data processing, and energy efficiency.

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:

• Grasp 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.

Customization Options for the Course

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

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

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

Upon completion of this training, participants will be equipped 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 to enable real-time decision-making.
• Integrate intelligent robotic systems into smart factory workflows.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical activities.
• Hands-on implementation in a live laboratory environment.

Customization Options

• For customized training requests for this course, please contact us to arrange details.