Autonomous Navigation & SLAM with ROS 2 Training Course

This instructor-led course in Uzbekistan focuses on applying computer vision to robotics through OpenCV and deep learning. Participants will master the implementation of perception pipelines, the integration of detection models, and the deployment of systems on embedded platforms to achieve autonomous control.

This live course in Uzbekistan assists intermediate engineers in implementing TinyML models for robotics. Discover techniques to optimize inference for speed and energy efficiency, integrate AI into control systems, and construct autonomous, low-latency robotic solutions directly on embedded hardware.

This instructor-led, live training in Uzbekistan (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.

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.

This practical HRI course in Uzbekistan explores voice, gesture, and collaborative control methods. It guides learners in constructing intuitive, multimodal interfaces that facilitate safe human-robot collaboration, emphasizing hands-on design principles, speech recognition, gesture detection, and shared autonomy to optimize both usability and safety.

This practical training on Uzbekistan explores ROS-PLC integration and digital twins within the context of industrial automation. Participants will learn to synchronize robotic systems with PLCs, create digital replicas for simulation purposes, and streamline production workflows through real-time data exchange and advanced monitoring strategies.

This instructor-led, live training in Uzbekistan (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.

An advanced training in Uzbekistan that focuses on the design and synchronization of multi-robot ecosystems. Learners will acquire the skills to model swarm intelligence, deploy decentralized decision-making processes, and refine agent collaboration through hands-on simulation with ROS 2 and Gazebo, targeting practical real-world deployments.

This instructor-led, live training in Uzbekistan (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.

This live, instructor-led program on Uzbekistan, accessible online or onsite, is tailored for intermediate-level professionals aiming to elevate their capabilities in designing, programming, and deploying intelligent robotic systems for automation and beyond.

By the end of the session, participants will have the proficiency to:

• Comprehend the foundational principles of Physical AI and their relevance in robotics and automation.
• Design and program intelligent robotic systems suited for dynamic environments.
• Integrate AI models to facilitate autonomous decision-making capabilities in robots.
• Employ simulation tools to enhance robotic testing and optimization processes.
• Manage critical technical challenges, such as sensor fusion, real-time processing, and energy conservation.

This instructor-led, practical session on Uzbekistan focuses on rapid prototyping in robotics via ROS 2 and Docker. Participants acquire hands-on competencies in containerization, simulation, and version control to construct, test, and deploy modular robotic systems for scalable development.

This instructor-led live training in Uzbekistan covers reinforcement learning principles and deep RL techniques like PPO and DDPG. Participants will implement algorithms using Python and PyTorch, integrate them with ROS 2 and OpenAI Gym, and train autonomous robots to solve complex tasks.

An advanced training program on Uzbekistan that applies deep learning to robotic manipulation. Gain proficiency in perception, grasp planning, and control through the use of neural networks and ROS 2. Practical simulation exercises are designed to prepare you for the effective deployment of intelligent and adaptable grasping solutions on physical robotic arms.