Human-Centric Physical AI: Collaborative Robots and Beyond 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.

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.

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.

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.

This instructor-led, live training in Turkey (online or onsite) is designed for intermediate-level professionals seeking to enhance their skills in designing, programming, and deploying intelligent robotic systems for automation and related fields.

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

• Grasp the core principles of Physical AI and its applications in robotics and automation.
• Design and program intelligent robotic systems suitable for dynamic environments.
• Deploy AI models to enable autonomous decision-making within robots.
• Utilize simulation tools for testing and optimizing robotic performance.
• Tackle challenges such as sensor fusion, real-time data processing, and energy efficiency.

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.

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

This live training session, led by an instructor and available either online or onsite, is designed for advanced machine learning engineers, robotics researchers, and developers eager to design, implement, and deploy reinforcement learning algorithms within robotic applications.

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

• Grasp the fundamental principles and mathematical underpinnings of reinforcement learning.
• Implement key 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 by leveraging deep learning frameworks such as PyTorch.

Course Format

• Engaging lectures paired with interactive discussions.
• Practical implementation exercises using Python, PyTorch, and OpenAI Gym.
• Hands-on practice within simulated or physical robotic environments.

Customization Options

• For those interested in a tailored training experience, please reach out to us to arrange a customized course.

This advanced course bridges the gap between robotic control and modern machine learning techniques. Participants will investigate how deep learning can improve perception, motion planning, and dexterous grasping within robotic systems. By combining theoretical knowledge with simulation and practical coding exercises, the curriculum guides learners from perception-based control methods toward end-to-end policy learning for complex manipulation tasks.

Delivered as an instructor-led live training (available online or onsite), this program is designed for advanced professionals aiming to apply deep learning methods to achieve intelligent, adaptable, and highly precise robotic manipulation.

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

• Building perception models for object recognition and pose estimation.
• Training neural networks to detect grasps and plan motions.
• Integrating deep learning modules with robotic controllers using ROS 2.
• Simulating and evaluating grasping and manipulation strategies in virtual environments.
• Deploying and optimizing learned models on physical or simulated robotic arms.

Course Format

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

Customization Options

• To request a customized training session for this course, 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.