Physical AI for Robotics and Automation 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 such as ROS 2, TensorFlow, and OpenCV, engineers can now 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 wish to develop, train, and deploy AI-driven robotic systems using current open-source technologies and frameworks for government applications. 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 using 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 simulated and real robotic environments. **Course Customization Options** To request a customized training for government, please contact us to arrange.

This instructor-led, live training for government (online or onsite) will equip participants with the knowledge and skills needed to program various types of robots for use in nuclear technology and environmental systems.

The 6-week course is held 5 days a week. Each session is 4 hours long and includes lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete real-world projects relevant to their work to apply the knowledge they have acquired.

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

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

• Understand key concepts in robotic technologies.
• Manage the interaction between software and hardware in a robotic system.
• Implement the software components essential for robotics.
• Build and operate a simulated mechanical robot capable of visual perception, sensing, processing, navigation, and voice interaction with humans.
• Grasp the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to developing intelligent robots.
• Implement filters such as Kalman and Particle to enable a robot to track moving objects in its environment.
• Develop search algorithms and motion planning strategies.
• Apply PID controls to manage a robot's movement within an environment.
• Implement SLAM (Simultaneous Localization and Mapping) algorithms to allow a robot to map out unknown environments.
• Enhance a robot’s capabilities through the use of Deep Learning techniques.
• Test and troubleshoot robots in realistic scenarios.

This instructor-led, live training for government (online or onsite) will cover the various technologies, frameworks, and techniques required for programming robots used in nuclear technology and environmental systems.

The four-week course is held five days a week, with each session lasting four hours. It includes lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete real-world projects applicable to their work to practice the knowledge they acquire.

For this course, the target hardware will be simulated in 3D using simulation software. The code developed will then be deployed onto physical hardware (such as Arduino or other platforms) for final testing. The ROS (Robot Operating System) open-source framework, along with C++ and Python, will be utilized for programming the robots.

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

• Understand the key concepts used in robotic technologies.
• Manage the interaction between software and hardware in a robotic system.
• Implement the software components that underpin robotics.
• Build and operate a simulated mechanical robot capable of seeing, sensing, processing, navigating, and interacting with humans through voice commands.
• Understand and apply the necessary elements of artificial intelligence (including machine learning and deep learning) to build smart robots.
• Implement filters such as Kalman and Particle to enable the robot to locate moving objects in its environment.
• Develop search algorithms and motion planning strategies.
• Apply PID controls to regulate a robot's movement within an environment.
• Implement SLAM (Simultaneous Localization and Mapping) 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 for government and industry use.

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 for government projects.

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

• Set up and configure ROS 2 for autonomous navigation applications in governmental settings.
• Implement SLAM algorithms for mapping and localization suitable for public sector environments.
• Integrate sensors such as LiDAR and cameras with ROS 2 to enhance situational awareness in government operations.
• Simulate and test autonomous navigation in Gazebo, a tool crucial for validating systems before deployment.
• Deploy navigation stacks on physical robots used in various public sector workflows.

Format of the Course

• Interactive lecture and discussion focused on government applications.
• Hands-on practice using ROS 2 tools and simulation environments relevant to public sector needs.
• Live-lab implementation and testing on virtual or physical robots tailored for government use cases.

Course Customization Options

• To request a customized training for this course, please contact us to arrange specific modules aligned with your agency's requirements.

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

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 fundamentals of intelligent bots
• Learn how to develop intelligent bots using cloud applications for government
• Gain proficiency in using the Microsoft Bot Framework, the Bot Builder SDK, and the Azure Bot Service
• Comprehend the design principles of bots through bot patterns
• Create their first intelligent bot using Microsoft Azure

Audience

• Developers
• Hobbyists
• Engineers
• IT Professionals

Format of the Course

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

OpenCV is an open-source computer vision library that supports real-time image processing, while deep learning frameworks such as TensorFlow provide the tools necessary for intelligent perception and decision-making in robotic systems for government applications.

This instructor-led, live training (online or onsite) is designed for intermediate-level robotics engineers, computer vision practitioners, and machine learning engineers who wish to apply computer vision and deep learning techniques for enhancing robotic perception and autonomy within public sector workflows.

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

• Implement computer vision pipelines using OpenCV in alignment with government standards.
• Integrate deep learning models for object detection and recognition, ensuring compliance with regulatory requirements.
• Utilize vision-based data for robotic control and navigation in public sector environments.
• Combine classical vision algorithms with deep neural networks to optimize performance in government projects.
• Deploy computer vision systems on embedded and robotic platforms, adhering to governance and accountability protocols.

Format of the Course

• Interactive lectures and discussions focused on public sector applications.
• Hands-on practice using OpenCV and TensorFlow, tailored to government scenarios.
• Live-lab implementation on simulated or physical robotic systems relevant to government operations.

Course Customization Options

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

A chatbot or bot serves as a digital assistant designed to automate user interactions across various messaging platforms, enabling tasks to be completed more efficiently without requiring direct human-to-human communication. In this instructor-led, live training, participants will gain the skills necessary to begin developing bots by working through the creation of sample chatbots using specialized development tools and frameworks. By the end of this training, participants will be able to: - Understand the diverse applications and uses of bots - Grasp the comprehensive process involved in bot development - Examine the various tools and platforms utilized in building bots - Construct a sample chatbot for Facebook Messenger - Develop a sample chatbot using Microsoft Bot Framework **Audience** - Developers interested in creating their own bots for government or private sector use **Format of the Course** - Part lecture, part discussion, with exercises and extensive hands-on practice

Edge AI enables artificial intelligence models to run directly on embedded or resource-constrained devices, reducing latency and power consumption while enhancing autonomy and privacy in robotic systems. This instructor-led, live training (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 for government applications. By the end of this training, participants will be able to: - Understand the fundamentals of TinyML and edge AI for robotics. - Convert and deploy AI models for on-device inference. - Optimize models for speed, size, and energy efficiency. - Integrate edge AI systems into robotic control architectures. - Evaluate performance and accuracy in real-world scenarios. **Format of the Course** - 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 (online or onsite) is designed for government participants at the intermediate level 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 for government operations. - Identify and address challenges in human-machine interactions within public sector environments. - Design workflows that optimize collaboration between humans and AI-driven systems for government use. - Promote a culture of innovation and adaptability in AI-integrated workplaces for government agencies.

This instructor-led, live training (online or onsite) is designed for government 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 for government applications. - Understand the concepts of neural networks and various learning methods. - Select appropriate artificial intelligence approaches effectively for real-life problems in the public sector. - Implement AI applications in mechatronic engineering projects for government.

This instructor-led, live training (available online or on-site) is designed for advanced-level robotics engineers and AI researchers who aim to utilize Multimodal AI for government applications. The goal is to integrate various sensory data to create more autonomous and efficient robots capable of seeing, hearing, and touching. 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.

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 designed for advanced-level machine learning engineers, robotics researchers, and developers who wish to design, implement, and deploy reinforcement learning algorithms in robotic applications for government. 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.

Safe & Explainable Robotics is a comprehensive training program focused on the safety, verification, and ethical governance of robotic systems. The course bridges theoretical knowledge with practical application by delving into safety case methodologies, hazard analysis, and explainable AI approaches that ensure transparency and trustworthiness in robotic decision-making. Participants will learn how to ensure compliance, verify behaviors, and document safety assurance in alignment with international standards. This instructor-led, live training (online or onsite) is designed for 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 for Government** To request a customized training for government agencies, please contact us to arrange.

A Smart Robot is an Artificial Intelligence (AI) system capable of learning from its environment and experiences, enhancing its capabilities through accumulated knowledge. These robots can collaborate with humans, working alongside them and adapting to human behaviors. They are adept at both manual tasks and cognitive functions. In addition to physical robots, Smart Robots can also exist as software applications, operating within a computer without any physical interaction. This instructor-led training provides participants with an in-depth understanding of the technologies, frameworks, and techniques required for programming mechanical Smart Robots. Participants will 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 laboratory environment. Each section concludes with a practical project to reinforce the skills learned. The target hardware for this training will be simulated in 3D using 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: - Understand the fundamental concepts in robotic technologies. - Manage the interaction between software and hardware in a robotic system. - Implement the software components essential for Smart Robots. - Build and operate a simulated mechanical Smart Robot capable of seeing, sensing, processing, grasping, navigating, and interacting with humans through voice commands. - Enhance a Smart Robot's capabilities to perform complex tasks using Deep Learning techniques. - Test and troubleshoot a Smart Robot in realistic scenarios. **Audience** - Developers - Engineers **Format of the Course** - Part lecture, part discussion, exercises, and extensive hands-on practice **Note** To tailor any aspect of this course (such as programming language or robot model) to specific needs for government projects, please contact us to arrange customization.

Smart Robotics involves the integration of artificial intelligence into robotic systems to enhance perception, decision-making, and autonomous control. This instructor-led, live training (available online or on-site) is designed for advanced-level robotics engineers, systems integrators, and automation leads who aim to implement AI-driven perception, planning, and control in smart manufacturing environments for government and industry applications. 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 sessions - Hands-on implementation in a live-lab environment **Course Customization Options** - To request a customized training for this course, please contact us to arrange.