Online AI for Robotics Training in Taiwan

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.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a hands-on 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: ROS-PLC Integration & Digital Twins is a practical course designed to bridge the gap between industrial automation and modern robotics frameworks. Participants will gain the skills to integrate ROS-based robotic systems with PLCs for synchronized operations and explore 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) is targeted at intermediate-level professionals who wish to develop 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 the 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.

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

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals seeking to apply deep learning methods to achieve intelligent, adaptable, and precise robotic manipulation.

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

• Develop perception models for object recognition and pose estimation.
• Train neural networks for grasp detection and motion planning.
• Integrate deep learning modules with robotic controllers using ROS 2.
• Simulate and evaluate grasping and manipulation strategies in virtual environments.
• Deploy and optimize learned models on real or simulated robotic arms.

Course Format

• Expert-led lectures and deep dives into algorithms.
• Hands-on coding and simulation exercises.
• Project-based implementation and testing.

Course Customization Options

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

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 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:

• 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.

Course Format

• Advanced lectures with in-depth algorithmic explorations.
• 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 is a framework for deploying machine learning models on low-power microcontrollers and embedded platforms used in robotics and autonomous systems.

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

Upon finishing this course, participants will be able to:

• Design optimized TinyML models for robotics applications.
• Implement on-device perception pipelines for 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 and Explainable Robotics is a comprehensive training program centered on the safety, verification, and ethical governance of robotic systems. This 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 alignment with international standards.

This instructor-led, live training (available 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-limited devices, thereby reducing latency and power usage while enhancing the autonomy and privacy of robotic systems.

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

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

• Comprehend the core principles of TinyML and edge AI for robotics.
• Convert and deploy AI models for on-device inference.
• Optimize models for speed, compactness, and energy efficiency.
• Integrate edge AI systems into robotic control architectures.
• Evaluate performance and accuracy in real-world scenarios.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Taiwan (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.

Reinforcement learning (RL) constitutes a machine learning paradigm wherein agents acquire decision-making capabilities by interacting with their environment. In the context of robotics, RL empowers autonomous systems to cultivate adaptive control and decision-making skills through experiential learning and feedback loops.

This instructor-led live training, available both online and on-site, is designed for advanced-level machine learning engineers, robotics researchers, and developers seeking to design, implement, and deploy reinforcement learning algorithms within 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 within simulated or physical robotic environments.

Course Customization Options

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

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.

This instructor-led, live training in Taiwan (online or onsite) is designed for advanced robotics engineers and AI researchers who aim to leverage Multimodal AI to integrate diverse sensory data, creating 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.
• Build robots capable of performing complex tasks in dynamic environments.
• Address challenges associated with real-time data processing and actuation.

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

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

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

• Understand and apply AI techniques for robotic perception and sensor fusion.
• Develop motion planning algorithms for 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 lecture and discussion.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

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 instructor-led, live training in Taiwan (online or onsite) is designed for intermediate-level participants who wish 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:

• 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.

The integration of Artificial Intelligence (AI) into 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 are now empowered to design robots that can intelligently navigate, plan, and interact with real-world environments.

This instructor-led live training, available either online or onsite, is tailored for intermediate-level engineers seeking to develop, train, and deploy AI-driven robotic systems using contemporary 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 for precise localization and tracking.
• Apply computer vision techniques via OpenCV for enhanced perception and object detection.
• 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 refine robotic decision-making processes.

Course Format

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

Customization Options

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

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

In this instructor-led live training, participants will learn how to begin developing bots by stepping through the creation of sample chatbots using dedicated development tools and frameworks.

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

• Grasp the diverse uses and applications of bots
• Understand the entire bot development lifecycle
• Explore various tools and platforms used for 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

Course Format

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

This instructor-led live training in Taiwan (online or onsite) is tailored for engineers interested in applying artificial intelligence to mechatronic systems.

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

• Obtain a comprehensive understanding of artificial intelligence, machine learning, and computational intelligence.
• Grasp the fundamental concepts of neural networks and various learning methodologies.
• Effectively select appropriate artificial intelligence approaches for real-world challenges.
• Implement AI solutions within mechatronic engineering contexts.