Safe & Explainable Robotics: Verification, Safety Cases & Ethics Training Course

Artificial Intelligence (AI) for Robotics integrates machine learning, control systems, and sensor fusion to create intelligent machines that can perceive, reason, and act autonomously. Utilizing modern tools like ROS 2, TensorFlow, and OpenCV, engineers are now able to design robots capable of navigating, planning, and interacting with real-world environments in an intelligent manner.

This instructor-led, live training (online or onsite) is designed for intermediate-level engineers who aim to develop, train, and deploy AI-driven robotic systems using the latest open-source technologies and frameworks.

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

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

Format of the Course

• Interactive lecture and discussion.
• Hands-on implementation using ROS 2 and Python.
• Practical exercises with both simulated and real robotic environments.

Course Customization Options

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

In this instructor-led, live training in Taiwan (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.

The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.

The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.

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.

In this instructor-led, live training in Taiwan (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.

The 4-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.

The target hardware for this course will be simulated in 3D through simulation software. The code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.

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.
• 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:

• Configure and set up ROS 2 for applications involving autonomous navigation.
• Implement SLAM algorithms to facilitate mapping and localization tasks.
• Integrate various sensors, such as LiDAR and cameras, with ROS 2.
• Simulate and test autonomous navigation scenarios in Gazebo.
• Deploy navigation stacks on actual robots.

Format of the Course

• Interactive lectures and discussions.
• Practical hands-on exercises 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.

The Azure Bot Service integrates the capabilities of the Microsoft Bot Framework and Azure Functions to facilitate the rapid development of intelligent bots.

In this instructor-led, live training, participants will learn how to efficiently create an intelligent bot using Microsoft Azure.

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

• Understand the basics of intelligent bots
• Learn how to develop intelligent bots using cloud applications
• Gain knowledge on utilizing the Microsoft Bot Framework, the Bot Builder SDK, and the Azure Bot Service
• Comprehend how to design bots using bot patterns
• Create their first intelligent bot using Microsoft Azure

Audience

• Developers
• Hobbyists
• Engineers
• IT Professionals

Format of the course

• Combination of lecture, discussion, practical exercises, and extensive hands-on practice

OpenCV is an open-source computer vision library that supports real-time image processing, while deep learning frameworks like TensorFlow offer the tools needed for intelligent perception and decision-making in robotic systems.

This instructor-led, live training (available online or on-site) is designed for intermediate-level robotics engineers, computer vision practitioners, and machine learning engineers who want to apply computer vision and deep learning techniques to enhance robotic perception and autonomy.

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

• Implement computer vision pipelines with OpenCV.
• Integrate deep learning models for object detection and recognition.
• Utilize 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.

Course Format

• Interactive lectures and discussions.
• 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 chatbot or bot acts as a digital assistant that automates user interactions on various messaging platforms, enabling tasks to be completed quickly without the need for human-to-human communication.

In this instructor-led, live training, participants will learn how to start developing a bot by creating sample chatbots using bot development tools and frameworks.

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

• Understand the diverse uses and applications of bots
• Grasp the entire process of developing bots
• Explore the different tools and platforms used in building bots
• Create a sample chatbot for Facebook Messenger
• Create a sample chatbot using Microsoft Bot Framework

Audience

• Developers interested in creating their own bot

Format of the course

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

Edge AI enables artificial intelligence models to run directly on embedded or resource-constrained devices, thereby reducing latency and power consumption while enhancing autonomy and privacy in robotic systems.

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

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

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

Format of the Course

• Interactive lecture and discussion sessions.
• 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 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.

This instructor-led, live training in Taiwan (online or onsite) is aimed at 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.

This instructor-led, live training in Taiwan (online or onsite) is aimed at advanced-level robotics engineers and AI researchers who wish to utilize Multimodal AI for integrating various sensory data to create more autonomous and efficient robots that can see, hear, and touch.

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

• Implement multimodal sensing in robotic systems.
• Develop AI algorithms for sensor fusion and decision-making.
• Create robots that can perform complex tasks in dynamic environments.
• Address challenges in real-time data processing and actuation.

This instructor-led, live training in Taiwan (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.

Reinforcement learning (RL) is a machine learning approach where agents learn to make decisions by interacting with their environment. In the field of robotics, RL empowers autonomous systems to develop adaptive control and decision-making skills through experience and feedback.

This instructor-led, live training (available online or on-site) is designed for advanced-level machine learning engineers, robotics researchers, and developers who want 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 mathematical foundations 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 lectures and discussions.
• 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.

A Smart Robot is an Artificial Intelligence (AI) system capable of learning from its environment and experiences, enhancing its abilities based on the acquired knowledge. These robots can work alongside humans, collaborating and learning from their actions. They are adept at both manual tasks and cognitive functions. Beyond physical robots, Smart Robots can also exist as software applications within a computer, with no physical components or interactions.

In this instructor-led, live training, participants will explore various technologies, frameworks, and techniques for programming mechanical Smart Robots. They will then apply this knowledge to complete their own Smart Robot projects.

The course is structured into four sections, each comprising three days of lectures, discussions, and hands-on robot development in a live lab setting. Each section concludes with a practical, hands-on project to reinforce the participants' newly acquired skills.

The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, along with C++ and Python, will be used for programming the robots.

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

• Comprehend the fundamental concepts of robotic technologies
• Understand and manage the interaction between software and hardware in a robotic system
• Implement the software components essential for Smart Robots
• Create and operate a simulated mechanical Smart Robot that can see, sense, process, grasp, navigate, and interact with humans through voice commands
• Enhance a Smart Robot's capabilities to perform complex tasks using Deep Learning
• Test and troubleshoot a Smart Robot in realistic scenarios

Audience

• Developers
• Engineers

Format of the course

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

Note

• To customize any aspect of this course (such as programming language or robot model), please contact us to arrange.

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

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

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 both collaborative and industrial robots.
• Implement 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.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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