AI for Robotics Training in Tashkent

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 emphasises agility, version control, and collaborative 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 behaviour before hardware deployment.
• Collaborate effectively using containerised robotics projects.
• Apply continuous integration and deployment concepts in robotics pipelines.

Course Format

• Interactive lectures and demonstrations.
• Hands-on exercises with ROS 2 and Docker environments.
• Mini-projects focused on real-world robotic applications.

Course Customisation Options

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

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course designed to introduce participants to the design and implementation of intuitive interfaces for human–robot communication. The training combines theory, design principles, and programming practice to build natural and responsive interaction systems using speech, gesture, and shared control techniques. Participants will learn how to integrate perception modules, develop multimodal input systems, and design robots that safely collaborate with humans.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level participants who wish to design and implement human–robot interaction systems that enhance usability, safety, and user experience.

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

• Understand the foundations 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 for safe and 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 in simulation or real robotic environments.

Course Customization Options

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

Industrial Robotics Automation: ROS-PLC Integration & Digital Twins is a practical course designed to bridge traditional industrial automation with modern robotics frameworks. Participants will learn how to integrate ROS-based robotic systems with PLCs for seamless, synchronized operations and will explore digital twin environments to simulate, monitor, and optimise production processes. The course places strong emphasis on interoperability, real-time control, and predictive analysis using digital replicas of physical systems.

This instructor-led, live training (available online or on-site) is tailored for intermediate-level professionals seeking to develop hands-on skills in connecting ROS-controlled robots with PLC environments and implementing digital twins to enhance automation and manufacturing efficiency.

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

• Understand the communication protocols linking ROS and PLC systems.
• Implement real-time data exchange between robots and industrial controllers.
• Develop digital twins for monitoring, testing, and process simulation.
• Integrate sensors, actuators, and robotic manipulators into industrial workflows.
• Design and validate industrial automation systems using hybrid simulation environments.

Course Format

• Interactive lectures and architecture walkthroughs.
• Practical exercises integrating ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Course Customisation Options

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

Robot Manipulation and Grasping with Deep Learning is an advanced course that connects robotic control with modern machine learning techniques. Participants will explore how deep learning can improve perception, motion planning, and dexterous grasping in robotic systems. Through theory, simulation, and practical coding exercises, the course guides learners from perception-based control to end-to-end policy learning for manipulation tasks.

This instructor-led, live training (online or on-site) is designed for advanced-level professionals who wish to apply deep learning methods to enable intelligent, adaptable, and precise robotic manipulation.

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

• Develop perception models for object recognition and pose estimation.
• Train neural networks for grasp detection and motion planning.
• Integrate deep learning modules with robotic controllers using ROS 2.
• Simulate and evaluate grasping and manipulation strategies in virtual environments.
• Deploy and optimize learned models on real or simulated robotic arms.

Course Format

• Expert-led lectures and in-depth algorithmic explorations.
• Hands-on coding and simulation exercises.
• Project-based implementation and testing.

Course Customization Options

• To request a customized version of this course, please contact us to make arrangements.

Multi-Robot Systems and Swarm Intelligence is an advanced training program that delves into the design, coordination, and management of robotic teams inspired by natural swarm behaviors. Participants will gain insights into modeling interactions, enabling distributed decision-making, and optimizing collaboration among multiple agents. The course integrates theoretical knowledge with practical simulations, preparing learners for real-world applications in logistics, defense, search and rescue, and autonomous exploration.

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

By the end 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 challenges.

Format of the Course

• In-depth advanced lectures with algorithmic exploration.
• Hands-on coding and simulation using ROS 2 and Gazebo.
• Collaborative project applying swarm intelligence principles.

Course Customization Options

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

TinyML is a framework for deploying machine learning models on low-power microcontrollers and embedded platforms used in robotics and autonomous systems.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

Upon finishing this course, participants will be able to:

• Design optimized TinyML models for robotics applications.
• Implement on-device perception pipelines for real-time autonomy.
• Integrate TinyML into existing robotic control frameworks.
• Deploy and test lightweight AI models on embedded hardware platforms.

Format of the Course

• Technical lectures combined with interactive discussions.
• Hands-on labs focusing on embedded robotics tasks.
• Practical exercises simulating real-world autonomous workflows.

Course Customization Options

• For organization-specific robotics environments, customization can be arranged upon request.

Safe & Explainable Robotics is an in-depth training program designed to address the safety, verification, and ethical governance of robotic systems. This course connects theoretical concepts with practical applications, delving into safety case methodologies, hazard analysis, and explainable AI techniques to ensure transparent and reliable robotic decision-making. Participants will gain the skills to verify system behaviors, ensure compliance, and document safety assurance in alignment with global standards.

This instructor-led training, available online or onsite, is tailored for intermediate-level professionals aiming to implement verification, validation, and explainability principles to guarantee the safe and ethical deployment of robotic systems.

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

• Create and document safety cases for robotic and autonomous systems.
• Utilize verification and validation techniques within simulation environments.
• Understand explainable AI frameworks for decision-making in robotics.
• Incorporate safety and ethical principles into system design and operation.
• Communicate safety and transparency requirements effectively to stakeholders.

Format of the Course

• Interactive lectures and discussions.
• Hands-on simulation and safety analysis exercises.
• Case studies based on real-world robotics applications.

Course Customization Options

• To request a customized version of this course, please contact us for arrangements.

Edge AI allows artificial intelligence models to operate directly on embedded or resource-limited devices, minimizing latency and power usage 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 want 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:

• Grasp 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.
• Assess 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 in Tashkent (online or in-person) is designed for 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.

Reinforcement learning (RL) represents a machine learning paradigm where agents acquire decision-making skills through interaction with their environment. Within the field of robotics, RL empowers autonomous systems to cultivate adaptive control and decision-making capabilities by leveraging experience and feedback.

This live training, conducted by an instructor in either an online or onsite setting, is designed for advanced machine learning engineers, robotics researchers, and developers who aim to design, implement, and deploy reinforcement learning algorithms within robotic applications.

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

• Grasp the fundamental principles and mathematical underpinnings of reinforcement learning.
• Implement RL algorithms including Q-learning, DDPG, and PPO.
• Integrate RL with robotic simulation environments utilizing OpenAI Gym and ROS 2.
• Enable robots to execute complex tasks autonomously through trial and error.
• Enhance training performance using deep learning frameworks such as PyTorch.

Course Format

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

Customization Options

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

OpenCV is a widely-used open-source computer vision library that facilitates real-time image processing. Deep learning frameworks like TensorFlow equip robotic systems with intelligent perception and decision-making capabilities.

This instructor-led, live training (available online or onsite) is designed for intermediate-level robotics engineers, computer vision specialists, and machine learning engineers who aim to leverage computer vision and deep learning for robotic perception and autonomy.

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

• Develop computer vision pipelines using OpenCV.
• Integrate deep learning models for object detection and recognition.
• Utilize vision-based data for robotic control and navigation.
• Combine traditional vision algorithms with deep neural networks.
• Deploy computer vision systems on embedded and robotic platforms.

Format of the Course

• Interactive lectures and discussions.
• Hands-on practice with OpenCV and TensorFlow.
• Live-lab implementation on simulated or physical robotic systems.

Course Customization Options

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

This instructor-led, live training in Tashkent (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.

Smart Robotics is the integration of artificial intelligence into robotic systems for improved perception, decision-making, and autonomous control.

This instructor-led, live training (online or onsite) is aimed at advanced-level robotics engineers, systems integrators, and automation leads who wish 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 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.

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:

• 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.

Format of the Course

• 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.

This instructor-led, live training in Tashkent (available online or on-site) is designed for intermediate-level participants aiming to strengthen their expertise in designing, programming, and deploying intelligent robotic systems for automation and beyond.

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 tailored for dynamic environments.
• Implement AI models to enable autonomous decision-making in robotic systems.
• Utilise simulation tools for robotic testing and performance optimisation.
• Overcome key challenges including sensor fusion, real-time processing, and energy efficiency.

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

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

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

• Utilise Python and ROS 2 to build and simulate robotic behaviours.
• Implement Kalman and Particle Filters for localisation and tracking.
• Apply computer vision techniques using OpenCV for perception and object detection.
• Leverage TensorFlow for motion prediction and learning-based control.
• Integrate SLAM (Simultaneous Localisation and Mapping) for autonomous navigation.
• Develop reinforcement learning models to enhance robotic decision-making.

Course Format

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

Course Customisation Options

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

In this instructor-led, live training in Tashkent (online or on-site), participants will explore the various technologies, frameworks, and techniques required to program different types of robots for use in nuclear technology and environmental systems.

This 6-week course runs 5 days a week, with each day comprising 4 hours of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete a series of real-world projects relevant to their work to apply and reinforce their newly acquired knowledge.

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

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

• Grasp the key concepts underpinning robotic technologies.
• Understand and manage the interaction between software and hardware within a robotic system.
• Understand and implement the software components that form the foundation of robotics.
• Build and operate a simulated mechanical robot capable of seeing, sensing, processing, navigating, and interacting with humans via voice.
• Understand the essential elements of artificial intelligence (such as machine learning and deep learning) applicable to building smart robots.
• Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
• Implement search algorithms and motion planning strategies.
• Implement PID controls to regulate a robot's movement within an environment.
• Implement SLAM algorithms to allow a robot to map an unknown environment.
• Enhance a robot's ability to perform complex tasks through Deep Learning.
• Test and troubleshoot robots in realistic scenarios.

Azure Bot Service unites the Microsoft Bot Framework and Azure Functions to deliver a robust platform for rapidly creating intelligent bots.

During this instructor-led live training, attendees will learn how to efficiently develop intelligent bots using Microsoft Azure.

Upon completing the training, participants will be able to:

Grasp the fundamental concepts of intelligent bots.

Develop intelligent bots using cloud-based applications.

Acquire hands-on knowledge of the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Implement established bot design patterns in real-world scenarios.

Create and deploy their initial intelligent bot using Microsoft Azure.

Target Audience

This course is tailored for developers, hobbyists, engineers, and IT professionals who are interested in bot development.

Course Format

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

A bot or chatbot functions like a computer assistant designed to automate user interactions across various messaging platforms, enabling faster task completion without the need for human intervention.

In this instructor-led live training, participants will learn how to begin developing bots by working through the creation of sample chatbots using dedicated bot development tools and frameworks.

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

• Understand the diverse uses and applications of bots
• Comprehend the entire process of bot development
• Explore the different tools and platforms utilized in building bots
• Construct a sample chatbot for Facebook Messenger
• Construct a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bot

Format of the course

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

This instructor-led, live training in Tashkent (available online or onsite) is designed for engineers seeking to understand how artificial intelligence can be applied to mechatronic systems.

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

• Gain a comprehensive overview of artificial intelligence, machine learning, and computational intelligence.
• Understand the fundamental concepts of neural networks and various learning methodologies.
• Effectively select appropriate artificial intelligence approaches to solve real-world problems.
• Implement AI-driven applications within the field of mechatronic engineering.

A Smart Robot is an Artificial Intelligence (AI) system capable of learning from its surroundings and experiences, enhancing its abilities based on acquired knowledge. These robots can collaborate with humans, working alongside them and adapting to their behavior. Beyond manual tasks, Smart Robots are also equipped to handle cognitive functions. They can exist as physical robots or purely software-based applications, operating within a computer without physical interaction with the world.

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

The course is structured into four sections, each spanning three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section concludes with a practical project, allowing participants to practice and showcase their newly acquired skills.

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 utilized for programming the robots.

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

• Grasp the fundamental concepts of robotic technologies
• Understand and manage the interaction between software and hardware in a robotic system
• Implement the software components that power 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 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 part of this course (programming language, robot model, etc.), please contact us for arrangements.