AI for Robotics Training in Hanoi

The "Practical Rapid Prototyping for Robotics with ROS 2 & Docker" course is a hands-on program designed to assist developers in building, testing, and deploying robotic applications efficiently. Participants will acquire the skills to containerize robotics environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker to ensure reproducibility and scalability. The curriculum places strong emphasis on agility, version control, and collaborative practices that are ideal for early-stage development and innovation teams.

This instructor-led live training is available both online and onsite, targeting beginner to intermediate participants who aim to accelerate their robotics development workflows using ROS 2 and Docker.

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

• Configure a ROS 2 development environment within Docker containers.
• Create and test robotic prototypes in modular, reproducible setups.
• Utilize simulation tools to validate system behavior prior to hardware deployment.
• Foster effective collaboration through containerized robotics projects.
• Implement continuous integration and deployment concepts within robotics pipelines.

Course Format

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

Customization Options

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

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.

Deep Learning-Driven Robotic Manipulation and Grasping is an advanced program that connects robotic control with cutting-edge machine learning methodologies. Participants will investigate how deep learning can improve perception, motion planning, and dexterous grasping within robotic systems. Through theoretical foundations, simulations, and practical coding tasks, the course guides learners from perception-based control to end-to-end policy learning for manipulation operations.

This instructor-led live training (available online or onsite) is designed for advanced professionals seeking to apply 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 for grasp detection and motion planning.
• Integrate deep learning components with robotic controllers using ROS 2.
• Simulate and assess grasping and manipulation strategies in virtual environments.
• Deploy and optimize learned models on physical or simulated robotic arms.

Course Format

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

Customization Options

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

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.

TinyML provides a framework for deploying machine learning models on low-power microcontrollers and embedded platforms, which are commonly used in robotics and autonomous systems.

This instructor-led live training (available online or onsite) is designed for advanced professionals who want to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

After completing this course, participants will be able to:

• Design optimized TinyML models for robotics applications.
• Implement on-device perception pipelines to enable real-time autonomy.
• Integrate TinyML into existing robotic control frameworks.
• Deploy and test lightweight AI models on embedded hardware platforms.

Format of the Course

• Technical lectures combined with interactive discussions.
• Hands-on labs focusing on embedded robotics tasks.
• Practical exercises simulating real-world autonomous workflows.

Course Customization Options

• For organization-specific robotics environments, customization can be arranged upon request.

Safe & Explainable Robotics is a comprehensive training focused on the safety, verification, and ethical governance of robotic systems. The course bridges theory and practice by exploring safety case methodologies, hazard analysis, and explainable AI approaches that make robotic decision-making transparent and trustworthy. Participants will learn how to ensure compliance, verify behaviors, and document safety assurance in line with international standards.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to apply verification, validation, and explainability principles to ensure the safe and ethical deployment of robotic systems.

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

• Develop and document safety cases for robotic and autonomous systems.
• Apply verification and validation techniques in simulation environments.
• Understand explainable AI frameworks for robotics decision-making.
• Integrate safety and ethics principles into system design and operation.
• Communicate safety and transparency requirements to stakeholders.

Format of the Course

• Interactive lecture and discussion.
• Hands-on simulation and safety analysis exercises.
• Case studies from real-world robotics applications.

Course Customization Options

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

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 Hanoi (online or onsite) targets 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.

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.

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.

This instructor-led, live training in Hanoi (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.

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.

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.

This live, instructor-led training in Hanoi (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.

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.

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

This instructor-led live training in Hanoi (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.