Human-Centric Physical AI: Collaborative Robots and Beyond Training Course

The intersection of Artificial Intelligence (AI) and Robotics leverages machine learning, control systems, and sensor fusion to build intelligent machines capable of autonomous perception, reasoning, and action. By utilizing contemporary tools such as ROS 2, TensorFlow, and OpenCV, engineers can design robots that intelligently navigate, plan routes, and interact with physical environments.

This instructor-led live training, available either online or on-site, targets intermediate-level engineers looking to develop, train, and deploy AI-powered robotic systems using modern 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.
• Deploy Kalman and Particle Filters for precise localization and tracking.
• Apply computer vision techniques via OpenCV for perception and object detection.
• Employ TensorFlow for motion prediction and learning-based control mechanisms.
• Integrate SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Create reinforcement learning models to enhance robotic decision-making capabilities.

Course Format

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

Customization Options

For information on arranging a customized training session for this course, please contact us directly.

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 library for computer vision, facilitating real-time image processing, while deep learning frameworks like TensorFlow supply the necessary tools for intelligent perception and decision-making within robotic systems.

This instructor-led, live training (available online or onsite) targets intermediate-level robotics engineers, computer vision specialists, and machine learning engineers aiming to apply computer vision and deep learning methodologies to enhance robotic perception and autonomy.

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

• Build computer vision pipelines utilizing OpenCV.
• Integrate deep learning models for object detection and recognition tasks.
• Leverage vision-based data to control and navigate robots.
• Merge classical vision algorithms with deep neural networks.
• Deploy computer vision solutions on embedded and robotic platforms.

Format of the Course

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

Course Customization Options

• To request a customized training session for this course, please reach out to us to make arrangements.

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

During this instructor-led live training, participants will learn how to begin developing a bot by stepping through the creation of sample chatbots using specific development tools and frameworks.

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

• Comprehend the various uses and applications of bots
• Understand the complete bot development lifecycle
• 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

Target Audience

• Developers interested in building their own bot

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 reduces latency and power usage while enhancing autonomy and privacy in robotic systems.

This instructor-led, live training (available online or onsite) targets intermediate-level embedded developers and robotics engineers aiming 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 fundamentals of TinyML and edge AI in 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.
• Evaluate performance and accuracy in real-world scenarios.

Course Format

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

Course Customization Options

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

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course aimed at introducing participants to the design and development of intuitive interfaces for human–robot communication. The training integrates theory, design principles, and programming practice to create natural and responsive interaction systems utilizing speech, gesture, and shared control methods. Participants will learn to integrate perception modules, develop multimodal input systems, and design robots that collaborate safely with humans.

This instructor-led, live training (available online or onsite) targets beginner to intermediate-level participants seeking to design and implement human–robot interaction systems that improve usability, safety, and user experience.

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

• Grasp 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: Integrating ROS with PLCs and Digital Twins is a practical, hands-on course designed to bridge the gap between industrial automation and contemporary robotics frameworks. Participants will learn how to seamlessly integrate ROS-based robotic systems with PLCs to achieve synchronized operations. The course also explores digital twin environments, enabling learners to simulate, monitor, and optimize production processes. A strong emphasis is placed on interoperability, real-time control, and predictive analysis utilizing digital replicas of physical systems.

This instructor-led live training (available online or onsite) targets intermediate-level professionals seeking to develop practical skills in connecting ROS-controlled robots with PLC environments and implementing digital twins to enhance automation and manufacturing efficiency.

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

• Grasp the communication protocols used between ROS and PLC systems.
• Implement real-time data exchange mechanisms between robots and industrial controllers.
• Create 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 architectural walkthroughs.
• Hands-on exercises focused on integrating ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Customization Options

• For a tailored training experience, please contact us to arrange your customized course.

This instructor-led live training in Bulgaria (online or onsite) is designed for engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems.

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

• Gain an overview of artificial intelligence, machine learning, and computational intelligence.
• Understand the concepts of neural networks and different learning methods.
• Choose artificial intelligence approaches effectively for real-life problems.
• Implement AI applications in mechatronic engineering.

Multi-Robot Systems and Swarm Intelligence is an advanced training program designed to delve into the architecture, coordination, and management of robotic teams, drawing inspiration from biological swarm dynamics. Participants will acquire skills in modeling interactions, executing distributed decision-making processes, and optimizing collaborative efforts across various agents. This course integrates theoretical foundations with practical simulation exercises to equip learners with the expertise needed for applications in logistics, defense, search and rescue operations, and autonomous exploration.

Delivered by an instructor, this live training is available both online and on-site, targeting advanced professionals who aim to design, simulate, and deploy multi-robot and swarm-based systems utilizing open-source frameworks and algorithms.

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

• Grasping the core principles and dynamics governing swarm intelligence and cooperative robotics.
• Developing communication and coordination strategies tailored for multi-robot environments.
• Executing distributed decision-making processes and consensus algorithms.
• Simulating collective behaviors including formation control, flocking, and coverage tasks.
• Applying swarm-based methodologies to real-world challenges and optimization problems.

Format of the Course

• Advanced lectures featuring deep dives into algorithms.
• Practical coding and simulation exercises using ROS 2 and Gazebo.
• A collaborative project focused on applying swarm intelligence principles.

Course Customization Options

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

This instructor-led, live training in Bulgaria (online or onsite) is designed for advanced robotics engineers and AI researchers aiming to utilize Multimodal AI. The objective is to integrate various sensory inputs to develop highly autonomous and efficient robots capable of seeing, hearing, and touching.

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 Bulgaria (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.

This hands-on course, "Practical Rapid Prototyping for Robotics with ROS 2 & Docker," is designed to assist developers in efficiently building, testing, and deploying robotic applications. Participants will acquire the skills to containerize robotics environments, integrate ROS 2 packages, and create modular robotic systems using Docker, ensuring both reproducibility and scalability. The curriculum emphasizes agility, version control, and collaborative practices ideal for early-stage development and innovation teams.

Delivered by an instructor, this live training (available online or onsite) targets beginner to intermediate-level participants aiming to accelerate their robotics development workflows using ROS 2 and Docker.

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

• Configure a ROS 2 development environment within Docker containers.
• Develop and test robotic prototypes in modular, reproducible setups.
• Utilize simulation tools to validate system behavior prior to hardware deployment.
• Collaborate effectively through containerized robotics projects.
• Implement continuous integration and deployment concepts within robotics pipelines.

Format of the Course

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

Course Customization Options

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

Reinforcement learning (RL) is a machine learning approach where agents acquire decision-making skills through interaction with their surroundings. In the field of robotics, RL empowers autonomous systems to develop adaptive control and decision-making capabilities by leveraging experience and feedback.

This instructor-led, live training, available either online or onsite, is designed for advanced-level 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 capable of:

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

Course Format

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

Customization Options

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

This advanced course, "Robotic Manipulation and Grasping Using Deep Learning," connects the principles of robotic control with contemporary machine learning methodologies. Students will investigate how deep learning can improve perception, motion planning, and dexterous grasping capabilities within robotic systems. By combining theoretical knowledge, simulations, and hands-on coding tasks, the curriculum leads learners from perception-driven control mechanisms to end-to-end policy learning for complex manipulation assignments.

This live training session, available both online and in person, is instructed by an expert and targets seasoned professionals seeking to utilize deep learning algorithms to create intelligent, adaptable, and precise robotic manipulation systems.

Upon completion of this training, participants will have mastered the following skills:

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

Course Format

• Lectures led by experts featuring deep dives into algorithms.
• Practical coding and simulation exercises.
• Implementation and testing through project-based learning.

Customization Options

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

Smart Robotics involves incorporating artificial intelligence into robotic systems to enhance 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.