Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control Training Course

AI for Robotics integrates machine learning, control systems, and sensor fusion to create intelligent machines that can perceive, reason, and act autonomously. Leveraging modern tools such as ROS 2, TensorFlow, and OpenCV, engineers are now able to design robots that navigate, plan, and interact with real-world environments intelligently.

This instructor-led live training (available online or onsite) is designed for intermediate-level engineers looking 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:

• Utilize Python and ROS 2 to construct and simulate robotic behaviors.
• Implement Kalman and Particle Filters for localization and tracking.
• Apply computer vision techniques with OpenCV for perception and object detection.
• Use TensorFlow for motion prediction and learning-based control.
• Integrate SLAM (Simultaneous Localization and Mapping) for autonomous navigation.
• Develop reinforcement learning models to enhance robotic decision-making.

Format of the Course

• Interactive lecture and discussion.
• Hands-on implementation using ROS 2 and Python.
• Practical exercises with simulated and real robotic environments.

Course Customization Options

To request 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.

OpenCV serves as an open-source computer vision library that facilitates real-time image processing, while deep learning frameworks like TensorFlow offer the necessary tools for enabling intelligent perception and decision-making within robotic systems.

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 techniques to enhance robotic perception and autonomy.

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

• Constructing computer vision pipelines using OpenCV.
• Integrating deep learning models for object detection and recognition tasks.
• Leveraging vision-based data for effective robotic control and navigation.
• Merging classical vision algorithms with deep neural networks.
• Deploying computer vision systems on both embedded and robotic platforms.

Course Format

• Interactive lectures and discussions.
• Practical exercises using OpenCV and TensorFlow.
• Live laboratory implementation on simulated or physical robotic systems.

Customization Options

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

A bot, or chatbot, functions as a virtual assistant designed to automate user interactions across various messaging platforms, enabling tasks to be completed more efficiently without requiring direct communication with a human agent.

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

Upon completing this training, participants will be able to:

• Identify various use cases and applications for bots
• Grasp the end-to-end process of bot development
• Examine the diverse tools and platforms available for building bots
• Construct a sample chatbot for Facebook Messenger
• Develop a sample chatbot utilizing the Microsoft Bot Framework

Audience

• Developers who wish to create 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, thereby minimizing latency and power usage while enhancing the autonomy and privacy of robotic systems.

This instructor-led, live training session, available online or on-site, is designed for intermediate-level embedded developers and robotics engineers looking to implement machine learning inference and optimization strategies 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 in 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.
• Assess performance and accuracy in real-world applications.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises using TinyML and edge AI toolchains.
• Practical work on embedded and robotic hardware platforms.

Customization Options

• To arrange a customized training for this course, please get in touch with us.

This instructor-led, live training in Slovakia (online or onsite) is designed for intermediate-level participants interested in examining the role of collaborative robots (cobots) and other human-centric AI systems within modern workplaces.

Upon completing this training, participants will be able to:

• Grasp the core principles of Human-Centric Physical AI and its practical applications.
• Investigate how collaborative robots contribute to improving workplace productivity.
• Recognize and resolve challenges associated with human-machine interactions.
• Develop workflows that maximize collaboration between humans and AI-driven systems.
• Foster a culture of innovation and adaptability in AI-integrated work environments.

Industrial Robotics Automation: Integrating ROS and PLCs with Digital Twins is a practical course designed to bridge the gap between industrial automation and modern robotics frameworks. Participants will gain expertise in integrating ROS-based robotic systems with PLCs for synchronized operations, while exploring digital twin environments to simulate, monitor, and optimize production processes. The curriculum emphasizes interoperability, real-time control, and predictive analysis using digital replicas of physical systems.

Delivered as instructor-led live training (available online or onsite), this program targets intermediate-level professionals seeking to develop practical skills in connecting ROS-controlled robots with PLC environments and implementing digital twins for automation and manufacturing optimization.

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

• Comprehend communication protocols utilized between ROS and PLC systems.
• Implement real-time data exchange mechanisms between robots and industrial controllers.
• Develop digital twins for monitoring, testing, and simulating processes.
• 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.
• Hands-on exercises focused on integrating ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Course Customization Options

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

This instructor-led live training, conducted Slovakia (online or onsite), targets engineers interested in exploring the application of artificial intelligence to mechatronic systems.

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

• Gain a broad understanding of artificial intelligence, machine learning, and computational intelligence.
• Understand the core concepts of neural networks and diverse learning methods.
• Effectively select artificial intelligence approaches for addressing practical challenges.
• Implement AI applications within mechatronic engineering contexts.

Multi-Robot Systems and Swarm Intelligence is an advanced training course that delves into the design, coordination, and control of robotic teams inspired by biological swarm behaviors. Participants will learn how to model interactions, implement distributed decision-making, and optimize collaboration across multiple agents. The course combines theory with hands-on simulation to prepare learners for applications in logistics, defense, search and rescue, and autonomous exploration.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to design, simulate, and implement multi-robot and swarm-based systems using open-source frameworks and algorithms.

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

• Understand 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 Slovakia (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 instructor-led, live training in Slovakia (online or onsite) is aimed at intermediate-level participants who wish to enhance their skills in designing, programming, and deploying intelligent robotic systems for automation and beyond.

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

• Understand the principles of Physical AI and its applications in robotics and automation.
• Design and program intelligent robotic systems for dynamic environments.
• Implement AI models for autonomous decision-making in robots.
• Leverage simulation tools for robotic testing and optimization.
• Address challenges such as sensor fusion, real-time 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) represents a machine learning approach where agents acquire decision-making skills by engaging with their surroundings. In the realm of robotics, RL empowers autonomous systems to build adaptive control and decision-making capacities by leveraging experience and feedback.

This instructor-led training, available online or onsite, targets advanced machine learning engineers, robotics researchers, and developers keen on designing, implementing, and deploying reinforcement learning algorithms for robotic applications.

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

• Grasping the core principles and mathematical foundations of reinforcement learning.
• Implementing RL algorithms including Q-learning, DDPG, and PPO.
• Integrating RL with robotic simulation environments via OpenAI Gym and ROS 2.
• Enabling robots to master complex tasks autonomously through trial and error.
• Enhancing training performance using deep learning frameworks such as PyTorch.

Course Format

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

Customization Options

• For personalized training arrangements, please get in touch with us.

The course 'Robotic Manipulation and Grasping with Deep Learning' serves as an advanced program that unites robotic control mechanisms with contemporary machine learning methodologies. Participants will investigate how deep learning can improve perception, motion planning, and dexterous grasping capabilities within robotic systems. Through theoretical study, simulation, and practical coding assignments, the course directs learners from perception-based control toward end-to-end policy learning for manipulation tasks.

This instructor-led live training, available both online and onsite, targets advanced professionals seeking to apply deep learning techniques to achieve intelligent, adaptable, and precise robotic manipulation.

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

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

Course Format

• Lectures and deep algorithmic analysis led by experts.
• Practical coding and simulation exercises.
• Project-based implementation and testing.

Customization Options

• For details regarding customized training for this course, please reach out to us to make arrangements.

Smart Robotics represents the fusion of artificial intelligence with robotic systems to enhance perception, decision-making capabilities, and autonomous control.

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

Upon completing this training, participants will be able to:

• Comprehend and apply AI techniques for robotic perception and sensor fusion.
• Create motion planning algorithms for both collaborative and industrial robots.
• Implement learning-based control strategies to enable real-time decision-making.
• Integrate intelligent robotic systems into smart factory operational workflows.

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and practical practice sessions.
• Hands-on implementation within a live-lab environment.

Course Customization Options

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