Artificial Intelligence (AI) for Robotics Training Course

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

In this instructor-led, live training in Serbia (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 4-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 code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. 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.
• Test and troubleshoot a robot in realistic scenarios.

ROS 2 (Robot Operating System 2) is an open-source framework created to facilitate the development of complex, scalable robotic applications.

This instructor-led live training, available both online and onsite, targets intermediate-level robotics engineers and developers who want to implement Simultaneous Localization and Mapping (SLAM) and autonomous navigation using ROS 2.

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

• Configure and set up ROS 2 for autonomous navigation applications.
• Implement SLAM algorithms to enable mapping and localization.
• Integrate ROS 2 with sensors such as LiDAR and cameras.
• Test and simulate autonomous navigation within Gazebo.
• Deploy navigation stacks onto physical robots.

Format of the Course

• Interactive lectures and discussions.
• Hands-on practice utilizing ROS 2 tools and simulation environments.
• Live-lab implementation and testing on either virtual or physical robots.

Course Customization Options

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

Azure Bot Service unites the capabilities of the Microsoft Bot Framework with Azure Functions, delivering a robust platform for rapidly constructing intelligent bots.

During this instructor-led, live training, participants will discover how to efficiently create intelligent bots using Microsoft Azure.

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

Grasping the fundamental concepts behind intelligent bots.

Constructing intelligent bots through cloud-based applications.

Acquiring practical expertise in the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Implementing established bot design patterns in real-world contexts.

Creating and deploying their first intelligent bot using Microsoft Azure.

Audience

This course is tailored for developers, hobbyists, engineers, and IT professionals with an interest in bot development.

Format of the course

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

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

This instructor-led training, available either online or onsite, is designed for intermediate-level robotics engineers, computer vision specialists, and machine learning professionals looking to leverage computer vision and deep learning techniques to enhance robotic perception and autonomy.

Upon completing this training, participants will be capable of:

• Building computer vision pipelines with OpenCV.
• Incorporating deep learning models for object detection and recognition.
• 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 either simulated or physical robotic systems.

Customization Options

• To arrange a tailored version of this course, please contact us.

A bot, or chatbot, functions as a virtual assistant designed to automate user interactions across various messaging platforms, enabling faster task completion without requiring direct communication with a human agent.

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

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

• Identify various use cases and applications for bots
• Grasp the end-to-end process of bot development
• Explore the tools and platforms utilized in bot construction
• Construct a sample chatbot for Facebook Messenger
• Develop a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bots

Course Format

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

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

This instructor-led live training (available online or onsite) targets intermediate 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.

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 enhance speed, reduce size, and improve 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 utilizing TinyML and edge AI toolchains.
• Practical exercises performed on embedded and robotic hardware platforms.

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.

This live, instructor-led training in Serbia (online or onsite) is intended for intermediate-level participants interested in examining 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.

The 'Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control' course offers a practical introduction to designing and implementing intuitive interfaces for communication between humans and robots. This training blends theoretical knowledge, design principles, and programming practice to create natural and responsive interaction systems utilizing speech, gestures, and shared control methods. Participants will gain skills in integrating perception modules, developing multimodal input systems, and designing robotic systems that safely collaborate with human users.

Delivered by an instructor either online or on-site, this training is tailored for beginners to intermediate-level attendees looking to design and implement human–robot interaction systems that improve usability, safety, and overall user experience.

Upon completing this training, participants will be capable of:

• Grasping the foundational concepts and design principles of human–robot interaction.
• Creating voice-based control and response mechanisms for robotic systems.
• Implementing gesture recognition through computer vision techniques.
• Designing collaborative control systems that ensure safe shared autonomy.
• Assessing HRI systems regarding usability, safety, and human factors.

Course Format

• Engaging lectures and live demonstrations.
• Practical coding and design exercises.
• Applied experiments conducted in simulation or real-world robotic environments.

Course Customization Options

• For customized training arrangements for this course, please contact us directly.

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

This instructor-led, live training (available online or onsite) is aimed at intermediate-level professionals who wish to build practical skills in connecting ROS-controlled robots with PLC environments and implementing digital twins for automation and manufacturing optimization.

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

• Understand the communication protocols used between 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 within industrial workflows.
• Design and validate industrial automation systems using hybrid simulation environments.

Format of the Course

• Interactive lectures and architecture walkthroughs.
• Hands-on exercises integrating ROS and PLC systems.
• Simulation and digital twin project implementation.

Course Customization Options

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

This instructor-led live training in Serbia (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.

Multi-Robot Systems and Swarm Intelligence is an advanced training course that investigates the design, coordination, and control of robotic teams inspired by biological swarm behaviors. Participants will acquire skills in modeling interactions, implementing distributed decision-making, and optimizing collaboration across multiple agents. The course blends theoretical concepts with practical simulation 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 who wish 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.
• Design 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 with algorithmic deep dives.
• Hands-on coding and simulation in 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.

This instructor-led, live training in Serbia (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.

A Smart Robot is an Artificial Intelligence (AI) system capable of learning from its environment and experiences, thereby enhancing its capabilities based on that acquired knowledge. These robots can collaborate with humans, working alongside them and observing their behavior to learn. Moreover, they are capable of performing not only physical labor but also cognitive tasks. Beyond physical hardware, Smart Robots can also be entirely software-based, existing within a computer as a software application 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 used to program different types of mechanical Smart Robots. They will then apply this knowledge to complete their own Smart Robot projects.

The course is divided into four sections, each comprising three days of lectures, discussions, and hands-on robot development within a live lab environment. Each section concludes with a practical hands-on project, allowing participants to practice and demonstrate their acquired knowledge.

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 key concepts underlying robotic technologies
• Understand and manage the interaction between software and hardware in a robotic system
• Understand and implement the software components that support Smart Robots
• Build 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 perform complex tasks through Deep Learning
• Test and troubleshoot a Smart Robot in realistic scenarios

Audience

• Developers
• Engineers

Format of the course

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

Note

• To customize any aspect of this course (e.g., programming language, robot model), please contact us to make arrangements.

Intelligent robotics involves embedding artificial intelligence into robotic frameworks to enhance perception, decision-making capabilities, and autonomous control.

This instructor-led live training, available both online and onsite, is designed for robotics engineers, systems integrators, and automation leaders seeking to implement AI-driven perception, planning, and control solutions within smart manufacturing settings.

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

• Grasp and apply AI methods for robotic perception and sensor fusion.
• Create motion planning algorithms tailored for collaborative and industrial robots.
• Implement learning-based control strategies to facilitate real-time decision making.
• Seamlessly integrate intelligent robotic systems into smart factory operations.

Training Format

• Engaging lectures and interactive discussions.
• Extensive exercises and practical practice.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For personalized training requests, please get in touch with us to arrange your specific needs.