Robot Learning & Reinforcement Learning in Practice Training Course

Artificial Intelligence (AI) for Robotics merges machine learning, control systems, and sensor fusion to build intelligent machines that can perceive, reason, and act autonomously. By leveraging modern tools such as ROS 2, TensorFlow, and OpenCV, engineers are now able to design robots that intelligently navigate, plan, and interact with real-world environments.

This instructor-led live training, available both online and onsite, is designed for intermediate-level engineers aiming to develop, train, and deploy AI-driven robotic systems using contemporary open-source technologies and frameworks.

Upon completion of this training, participants will be equipped 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 perception and object detection.
• Leverage TensorFlow for motion prediction and learning-based control.
• Integrate SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Develop reinforcement learning models to enhance robotic decision-making capabilities.

Format of the Course

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

Course Customization Options

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

In this instructor-led, live training in Turkey (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.

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 (available online or onsite) targets 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.

Course Format

• 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.

Azure Bot Service integrates the Microsoft Bot Framework with Azure Functions, offering a robust platform for rapidly constructing intelligent bots.

Through this instructor-led live training, attendees will discover how to effectively develop intelligent bots leveraging Microsoft Azure.

Upon completing the training, participants will be able to:

Grasp the fundamental concepts underlying intelligent bots.

Construct intelligent bots utilizing cloud-based applications.

Acquire hands-on expertise with the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Implement proven bot design patterns in practical situations.

Develop and deploy their first intelligent bot using Microsoft Azure.

Target Audience

This course is tailored for developers, enthusiasts, engineers, and IT specialists keen on bot development.

Course Format

The training merges lectures and discussions with exercises, placing a strong emphasis on practical, hands-on application.

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

This instructor-led live training, available both online and onsite, targets intermediate robotics engineers, computer vision professionals, and machine learning engineers looking to apply computer vision and deep learning methods to enhance robotic perception and autonomy.

Upon completion of this training, participants will be capable of:

• Building computer vision pipelines using OpenCV.
• Incorporating deep learning models for object detection and recognition tasks.
• Leveraging vision-based data for robotic control and navigation.
• Merging classical vision algorithms with deep neural networks.
• Deploying computer vision solutions on embedded and robotic platforms.

Course Format

• Interactive lectures and discussions.
• Practical exercises utilizing OpenCV and TensorFlow.
• Live-lab implementation on simulated or physical robotic setups.

Course Customization Options

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

A bot, or chatbot, functions as a digital assistant designed to automate user interactions across various messaging platforms, enabling faster task completion without requiring human intervention.

Through this instructor-led live training, participants will learn the fundamentals of bot development by building sample chatbots using industry-standard development tools and frameworks.

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

• Identify the diverse applications and use cases of bots
• Master the end-to-end process of bot development
• Evaluate the tools and platforms available for constructing bots
• Construct a sample chatbot for Facebook Messenger
• Develop a sample chatbot utilizing the Microsoft Bot Framework

Audience

• Developers aiming to create their own custom bots

Course Format

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

Edge AI allows artificial intelligence models to execute directly on embedded or resource-limited devices, which reduces latency and power usage while enhancing autonomy and privacy within robotic systems.

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

Upon completing this training, participants will be able to:

• Grasp the core principles of TinyML and edge AI 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 scenarios.

Course Format

• Interactive lectures and discussions.
• 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 Turkey (online or on-site) targets intermediate-level participants interested in exploring the function of collaborative robots (cobots) and other human-centric AI systems in today’s workplaces.

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

• Comprehend the principles of Human-Centric Physical AI and its real-world applications.
• Investigate the role of collaborative robots in boosting workplace productivity.
• Identify and resolve challenges inherent in human-machine interactions.
• Develop workflows that optimize collaboration between humans and AI-driven systems.
• Encourage a culture of innovation and adaptability in AI-integrated workplaces.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course designed to equip participants with the skills to design and implement intuitive interfaces for effective human–robot communication. This training blends theoretical foundations, design principles, and programming practice to create natural and responsive interaction systems utilizing speech, gesture, and shared control techniques. Participants will learn to integrate perception modules, develop multimodal input systems, and design robots that collaborate safely with humans.

This instructor-led, live training (available online or onsite) targets beginner to intermediate-level participants who aim to design and implement human–robot interaction systems that improve usability, safety, and overall user experience.

By the conclusion 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 that ensure safe 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 conducted in simulation or real robotic environments.

Course Customization Options

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

This hands-on course, Industrial Robotics Automation: ROS-PLC Integration & Digital Twins, is designed to bridge the gap between traditional industrial automation and modern robotics frameworks. Participants will gain practical skills in synchronizing ROS-based robotic systems with PLCs and utilizing digital twin environments to simulate, monitor, and optimize production workflows. The curriculum places a strong emphasis on system interoperability, real-time control mechanisms, and predictive analytics derived from digital replicas of physical assets.

Delivered as live, instructor-led training (available online or onsite), this program targets intermediate-level professionals seeking to master the connection between ROS-controlled robots and PLC environments. It also focuses on leveraging digital twins to enhance automation and manufacturing efficiency.

Upon completion, participants will be equipped to:

• Grasp the communication protocols linking ROS and PLC systems.
• Execute real-time data exchange between robotic units and industrial controllers.
• Create digital twins for process simulation, testing, and continuous monitoring.
• Seamlessly integrate sensors, actuators, and robotic manipulators into industrial operations.
• Design and validate automation systems using hybrid simulation setups.

Course Format

• Engaging lectures accompanied by architectural walkthroughs.
• Practical exercises focused on integrating ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Customization Options

• For customized training requests, please contact us to discuss your specific needs.

This instructor-led live training in Turkey (offered online or on-site) targets engineers interested in applying artificial intelligence to mechatronic systems.

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

• Gain a broad overview of artificial intelligence, machine learning, and computational intelligence.
• Understand neural network concepts and diverse learning methods.
• Select suitable artificial intelligence approaches for solving real-world problems.
• Implement AI applications in mechatronic engineering.

Multi-Robot Systems and Swarm Intelligence is an advanced training course that delves into the design, coordination, and control of robotic teams, drawing inspiration from biological swarm behaviors. Participants will learn how to model interactions, implement distributed decision-making processes, and optimize collaboration among multiple agents. This course blends theoretical concepts with practical simulation exercises to prepare learners for applications in logistics, defense, search and rescue, and autonomous exploration.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals aiming to design, simulate, and implement multi-robot and swarm-based systems using open-source frameworks and algorithms.

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

• Grasp the principles and dynamics of swarm intelligence and cooperative robotics.
• Develop 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.

Format of the Course

• Advanced lectures featuring algorithmic deep dives.
• Hands-on coding and simulation using ROS 2 and Gazebo.
• A collaborative project applying swarm intelligence principles.

Course Customization Options

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

This instructor-led, live training in Turkey (online or onsite) targets advanced robotics engineers and AI researchers seeking to leverage Multimodal AI to integrate diverse sensory data. The goal is to build 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.
• Create robots capable of executing complex tasks in dynamic environments.
• Tackle challenges related to real-time data processing and actuation.

A Smart Robot is an Artificial Intelligence (AI) system that acquires knowledge from its surroundings and past experiences, enhancing its abilities based on that acquired insight. These robots can work collaboratively with humans, operating alongside them and learning from their actions. Moreover, they are capable of performing not just manual labor but also cognitive tasks. Beyond physical hardware, Smart Robots can also exist as purely software-based applications within a computer, operating without moving parts or direct physical interaction with the world.

In this instructor-led live training, participants will explore the various technologies, frameworks, and techniques required to program different types of mechanical Smart Robots, applying this knowledge to complete their own Smart Robot projects.

The course is structured into 4 sections, each spanning three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section concludes with a practical, hands-on project, allowing participants to practice and demonstrate the knowledge they have acquired.

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

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

• Grasp the core concepts underlying robotic technologies
• Understand and manage the interaction between software and hardware within a robotic system
• Comprehend and implement the software components that support Smart Robots
• Construct and operate a simulated mechanical Smart Robot capable of seeing, sensing, processing, grasping, navigating, and interacting with humans via voice
• Enhance a Smart Robot's ability to execute complex tasks through Deep Learning
• Test and troubleshoot a Smart Robot in realistic scenarios

Audience

• Developers
• Engineers

Format of the course

• A combination of lectures, discussions, exercises, and intensive hands-on practice

Note

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

Smart Robotics involves the seamless integration of artificial intelligence into robotic systems to enhance perception, decision-making capabilities, 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 aim to implement AI-driven perception, planning, and control within 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 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.
• Lots of 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.