Veštačka inteligencija u robotici Training in Srbija

This hands-on, instructor-led training in Srbija explores rapid prototyping in robotics via ROS 2 and Docker. Participants gain practical skills in building, testing, and deploying modular robotic systems, with a focus on containerization, simulation, and version control to support scalable development.

This practical HRI course in Srbija focuses on voice, gesture, and collaborative control. Participants will learn to create intuitive, multimodal interfaces that facilitate safe human-robot collaboration, with a focus on practical design principles, speech recognition, gesture detection, and shared autonomy to improve usability and safety.

This practical training in Srbija addresses 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 enhance production workflows through real-time data exchange and sophisticated monitoring techniques.

An advanced training in Srbija focusing on deep learning for robotic manipulation. Acquire skills in perception, grasp planning, and control using neural networks and ROS 2. Practical simulation exercises prepare you to effectively deploy intelligent, adaptable grasping solutions on real robotic arms.

A specialized training in Srbija focused on the design and coordination of multi-robot systems. Participants will master modeling swarm intelligence, implementing distributed decision-making, and optimizing agent collaboration through hands-on simulation in ROS 2 and Gazebo, preparing for real-world applications.

This live, instructor-led training in Srbija guides advanced professionals in integrating TinyML into autonomous robotics. Participants will learn to design optimized models, implement on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.

This 21-hour instructor-led training in Srbija addresses safety cases, verification, and explainable AI within robotics. Discover how to apply hazard analysis, ethical governance, and compliance standards to ensure transparent and trustworthy robotic decision-making in real-world scenarios.

This live training in Srbija empowers intermediate engineers to deploy TinyML models for robotics. Discover how to optimize inference for speed and energy efficiency, integrate AI into control systems, and construct autonomous, low-latency robotic solutions directly on embedded hardware.

Ova obuka sa instruktorom uživo za Srbija (onlajn ili licem u lice) namenjena je učesnicima srednjeg nivoa znanja koji žele da istraže ulogu kolaborativnih robotova (kobota) i drugih sistema veštačke inteligencije usmerenih na čoveka u modernim radnim okruženjima.

Krajem ove obuke, učesnici će moći da:

• Razumeju principe fizičke AI usmerene na čoveka i njihove primene.
• Istraze ulogu kolaborativnih robotova u poboljšanju produktivnosti na radnom mestu.
• Prepoznaju i rešavaju izazove u interakciji čoveka i mašine.
• Dizajniraju tokove rada koji optimizuju saradnju između ljudi i sistema pokrećanih veštačkom inteligencijom.
• Promovišu kulturu inovacija i prilagodljivosti na radnim mestima integrisanim sa veštačkom inteligencijom.

Ova živa obuka koju vodi instruktor u Srbija pokriva principe jačanja učenja i tehnike dubokog RL, kao što su PPO i DDPG. Učesnici će implementirati algoritme koristeći Python i PyTorch, integrisati ih sa ROS 2 i OpenAI Gym, te obučavati autonomne robote da rešavaju složene zadatke.

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

This instructor-led, live training in Srbija (online or onsite) targets advanced 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.

In Srbija, this live session empowers advanced engineers with the AI competencies needed for robotic perception, planning, and control. Attendees will gain practical experience in applying sensor fusion, developing motion algorithms, and deploying learning-based strategies to effectively integrate intelligent systems into smart manufacturing operations.

This instructor-led, live training in Srbija empowers intermediate robotics engineers with practical expertise in autonomous navigation and SLAM via ROS 2. Participants will learn to integrate LiDAR sensors, execute mapping algorithms, perform simulations in Gazebo, and deploy functional navigation stacks on real-world robotic hardware.

This live, instructor-led training on Srbija—offered online or on-site—targets intermediate-level participants aiming to strengthen their capabilities in designing, programming, and deploying intelligent robotic systems for automation and beyond.

Upon completing this training, participants will be able to:

• Understand the core principles of Physical AI and their application in robotics and automation.
• Design and program intelligent robotic systems for dynamic environments.
• Implement AI models for autonomous decision-making in robots.
• Utilize simulation tools for robotic testing and optimization.
• Address challenges related to sensor fusion, real-time processing, and energy efficiency.

This instructor-led live training in Srbija (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:

• Acquire a comprehensive overview of artificial intelligence, machine learning, and computational intelligence.
• Comprehend the principles of neural networks and various learning methodologies.
• Select appropriate artificial intelligence strategies to address real-world challenges.
• Apply AI solutions within the field of mechatronic engineering.