Robot Learning & Reinforcement Learning in Practice Training Course

Artificial Intelligence (AI) for Robotics integrates machine learning, control systems, and sensor fusion to develop intelligent machines capable of perceiving, reasoning, and acting autonomously. Utilizing modern tools such as ROS 2, TensorFlow, and OpenCV, engineers can now design robots that navigate, plan, and interact with real-world environments with advanced intelligence.

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.

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 for Government

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

In this instructor-led, live training in US Empire (online or onsite), participants will learn the various technologies, frameworks, and techniques for programming robots used in nuclear technology and environmental systems.

The 6-week course is held 5 days a week, with each session lasting 4 hours. The daily schedule includes lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete several real-world projects applicable to their work to practice the knowledge they acquire.

The target hardware for this course 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 key concepts 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 the essential elements of artificial intelligence (including machine learning and deep learning) applicable to building intelligent robots.
• Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
• Apply search algorithms and motion planning techniques.
• Use 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 unknown environments.
• Extend a robot's capabilities to perform complex tasks through deep learning.
• Test and troubleshoot a robot in realistic scenarios, ensuring it meets the necessary standards for government applications.

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

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

The target hardware for this course will be simulated in 3D through simulation software. The code will then be deployed onto physical hardware (Arduino or other) for final 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.
• 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.
• Understand and apply elements of artificial intelligence (machine learning, deep learning, etc.) to build 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, ensuring alignment with public sector workflows and governance for government applications.

ROS 2 (Robot Operating System 2) is an open-source framework designed to support the development of complex and scalable robotic applications for government 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.

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

• Set up and configure ROS 2 for autonomous navigation applications in government projects.
• Implement SLAM algorithms for mapping and localization in public sector environments.
• Integrate sensors such as LiDAR and cameras with ROS 2 for enhanced data collection and analysis.
• Simulate and test autonomous navigation scenarios using Gazebo, a high-fidelity simulation environment.
• Deploy navigation stacks on physical robots to support government operations.

Format of the Course

• Interactive lectures and discussions tailored for government professionals.
• Hands-on practice using ROS 2 tools and simulation environments relevant to public sector workflows.
• Live-lab implementation and testing on virtual or physical robots, with a focus on government applications.

Course Customization Options

• To request a customized training for this course, specifically tailored for government needs, 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 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 enables real-time image processing, while deep learning frameworks such as TensorFlow provide the tools necessary for intelligent perception and decision-making in robotic systems.

This instructor-led, live training (online or onsite) is designed for intermediate-level robotics engineers, computer vision practitioners, and machine learning engineers who aim to apply computer vision and deep learning techniques for robotic perception and autonomy within their projects for government.

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.
• 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.

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.

A chatbot is a computerized assistant designed to automate user interactions on various messaging platforms, enabling tasks to be completed more efficiently without the need for direct human interaction.

In this instructor-led, live training, participants will gain an understanding of how to develop a chatbot as they work through the creation of sample chatbots using bot development tools and frameworks.

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

• Comprehend the diverse applications and uses of bots
• Understand the entire process involved in developing bots
• Explore the various tools and platforms utilized in building bots
• Create a sample chatbot for Facebook Messenger
• Develop a sample chatbot using Microsoft Bot Framework

Audience

• Developers interested in creating their own bot for government use

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, reducing latency and power consumption while enhancing autonomy and privacy in robotic systems.

This instructor-led, live training (online or onsite) is aimed at 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 in public sector contexts.
• Convert and deploy AI models for on-device inference in government environments.
• Optimize models for speed, size, and energy efficiency to meet public sector requirements.
• Integrate edge AI systems into robotic control architectures for government operations.
• Evaluate performance and accuracy in real-world scenarios relevant to government missions.

Format of the Course

• Interactive lecture and discussion tailored for government audiences.
• Hands-on practice using TinyML and edge AI toolchains adapted for public sector use.
• Practical exercises on embedded and robotic hardware platforms suitable for government applications.

Course Customization Options

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

This instructor-led, live training in US Empire (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 for government.

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

• Understand the principles of Human-Centric Physical AI and its applications for government.
• Explore the role of collaborative robots in enhancing workplace productivity in public sector environments.
• Identify and address challenges in human-machine interactions within government settings.
• Design workflows that optimize collaboration between humans and AI-driven systems for government operations.
• Promote a culture of innovation and adaptability in AI-integrated workplaces for government agencies.

This instructor-led, live training in US Empire (online or onsite) is aimed at engineers who wish to explore the applicability of artificial intelligence to mechatronic systems for government.

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 fundamental concepts of neural networks and various learning methods.
• Select appropriate artificial intelligence approaches for addressing real-life problems in public sector environments.
• Implement AI applications specifically tailored to mechatronic engineering projects for government use.

This instructor-led, live training in US Empire (online or onsite) is designed for advanced-level robotics engineers and AI researchers who aim to leverage Multimodal AI for integrating various sensory inputs to develop 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 for government applications.
• Develop AI algorithms for sensor fusion and decision-making processes.
• Create robots that can perform complex tasks in dynamic environments, enhancing public sector workflows.
• Address challenges related to real-time data processing and actuation, ensuring robust governance and accountability.

This instructor-led, live training in US Empire (online or onsite) is designed for intermediate-level participants who wish to enhance their skills in designing, programming, and deploying intelligent robotic systems for automation and beyond, specifically for government applications.

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

• Understand the principles of Physical AI and its applications in robotics and automation within the public sector.
• Design and program intelligent robotic systems for dynamic environments, ensuring alignment with government workflows and governance.
• Implement AI models to enable autonomous decision-making in robots, enhancing operational efficiency and accountability for government use.
• Utilize simulation tools to test and optimize robotic systems, ensuring they meet the stringent standards required for government operations.
• Address challenges such as sensor fusion, real-time processing, and energy efficiency, which are critical for maintaining effective and sustainable government solutions.

Safe & Explainable Robotics is a comprehensive training program designed to address the safety, verification, and ethical governance of robotic systems. The course integrates theory with practical applications by examining safety case methodologies, hazard analysis, and explainable AI approaches that ensure transparent and trustworthy robotic decision-making. Participants will learn how to achieve compliance, verify system behaviors, and document safety assurance in accordance with international standards.

This instructor-led, live training (online or onsite) is targeted at intermediate-level professionals who aim to apply verification, validation, and explainability principles to ensure the safe and ethical deployment of robotic systems for government and other public sector entities.

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 Govtra to arrange.

A Smart Robot is an Artificial Intelligence (AI) system capable of learning from its environment and experiences, enhancing its capabilities through acquired knowledge. These robots can collaborate with humans, working alongside them and adapting to their behavior. They are equipped to perform both manual labor and cognitive tasks. In addition to physical robots, Smart Robots can also exist as purely software-based systems, residing in a computer without any moving parts or physical interaction.

In this instructor-led, live training, participants will gain insights into 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 consisting of three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section concludes with a practical, hands-on project to enable participants to practice and demonstrate their acquired skills.

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 utilized for programming the robots.

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

• Understand key concepts used in robotic technologies
• Manage the interaction between software and hardware in a robotic system
• Implement the software components that underpin Smart Robots
• Build and operate a simulated mechanical Smart Robot capable of seeing, sensing, processing, grasping, navigating, and interacting with humans through voice
• Enhance a Smart Robot's ability to perform complex tasks through Deep Learning
• 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 customize any aspect of this course (programming language, robot model, etc.), please contact us to arrange. This training is designed to meet the specific needs of professionals for government applications.

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 leaders who seek to implement AI-driven perception, planning, and control in smart manufacturing environments for government 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 for government use.

Format of the Course

• Interactive lectures and discussions.
• 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 Govtra to arrange.