Practical Rapid Prototyping for Robotics with ROS 2 & Docker Training Course

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

This instructor-led live training, available both online and onsite, is designed for intermediate-level engineers who want 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 using OpenCV for perception and object detection.
• Employ 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 a customized training for this course, please contact us to arrange.

In this instructor-led live training held in Mexico (online or onsite), participants will learn the different technologies, frameworks, and techniques for programming various types of robots for use in nuclear technology and environmental systems.

The course spans 6 weeks, meeting 5 days a week. Each day consists of a 4-hour session including lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete real-world projects applicable to their work to practice their acquired knowledge.

The target hardware for this course will be simulated in 3D using simulation software. The open-source ROS (Robot Operating System) 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.

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 is an open-source computer vision library that enables real-time image processing, while deep learning frameworks such as TensorFlow provide the tools for intelligent perception and decision-making in robotic systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level robotics engineers, computer vision practitioners, and machine learning engineers who wish to apply computer vision and deep learning techniques for robotic perception and autonomy.

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

• Implement computer vision pipelines using OpenCV.
• Integrate deep learning models for object detection and recognition.
• Use vision-based data for robotic control and navigation.
• Combine classical vision algorithms with deep neural networks.
• Deploy computer vision systems on embedded and robotic platforms.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice using OpenCV and TensorFlow.
• Live-lab implementation on simulated or physical robotic systems.

Course Customization Options

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

A bot or chatbot is like a computer assistant that is used to automate user interactions on various messaging platforms and get things done faster without the need for users to speak to another human.

In this instructor-led, live training, participants will learn how to get started in developing a bot as they step through the creation of sample chatbots using bot development tools and frameworks.

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

• Understand the different uses and applications of bots
• Understand the complete process in developing bots
• Explore the different tools and platforms used in building bots
• Build a sample chatbot for Facebook Messenger
• Build a sample chatbot using Microsoft Bot Framework

Audience

• Developers interested in creating their own bot

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Edge AI allows artificial intelligence models to execute directly on embedded or resource-constrained devices, which reduces latency and power usage while boosting autonomy and privacy in robotic systems.

This instructor-led, live training (available online or onsite) targets intermediate-level embedded developers and robotics engineers looking 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 for 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 using 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 make arrangements.

This instructor-led, live training in Mexico (online or in-person) is designed for intermediate-level participants eager to examine how collaborative robots (cobots) and other human-centric AI systems are shaping modern work environments.

Upon completing this training, participants will be equipped to:

• Grasp the core principles of Human-Centric Physical AI and its practical applications.
• Examine how collaborative robots contribute to increased workplace efficiency.
• Recognize and resolve challenges related to human-machine interaction.
• Create workflows that maximize collaboration between human workers and AI-driven systems.
• Foster a workplace culture centered on innovation and adaptability within AI-integrated environments.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course designed to introduce participants to the design and implementation of intuitive interfaces for human–robot communication. The training combines theory, design principles, and programming practice to build natural and responsive interaction systems using speech, gesture, and shared control techniques. Participants will learn how to integrate perception modules, develop multimodal input systems, and design robots that safely collaborate with humans.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level participants who wish to design and implement human–robot interaction systems that enhance usability, safety, and user experience.

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

• Understand 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: ROS-PLC Integration & Digital Twins is a practical course designed to bridge industrial automation with contemporary robotics frameworks. Participants will learn 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 (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 communication protocols 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 lecture 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 Mexico (online or onsite) is aimed at 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 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:

• 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 Mexico (online or onsite) is designed for advanced robotics engineers and AI researchers who want to leverage Multimodal AI to integrate diverse sensory data. The goal is to build more autonomous and efficient robots that can perceive through sight, sound, and touch.

Upon completion 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 Mexico (online or onsite) is designed for intermediate-level participants who want to improve their skills in designing, programming, and deploying intelligent robotic systems for automation and other applications.

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

• Grasp the core principles of Physical AI and its uses in robotics and automation.
• Design and program intelligent robotic systems for dynamic environments.
• Implement AI models to enable autonomous decision-making in robots.
• Use simulation tools to test and optimize robotic performance.
• Tackle challenges such as sensor fusion, real-time processing, and energy efficiency.

Reinforcement learning (RL) represents 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 build adaptive control and decision-making abilities by leveraging experience and feedback.

This instructor-led live training, available both online and on-site, is designed for advanced 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 equipped to:

• Grasp the fundamental principles and mathematical foundations of reinforcement learning.
• Execute RL algorithms including Q-learning, DDPG, and PPO.
• Integrate RL with robotic simulation platforms using OpenAI Gym and ROS 2.
• Train robots to execute complex tasks autonomously via trial and error.
• Enhance training performance utilizing deep learning frameworks such as PyTorch.

Course Format

• Interactive lectures and discussions.
• Practical implementation exercises using Python, PyTorch, and OpenAI Gym.
• Applied sessions in simulated or physical robotic settings.

Customization Options

• To arrange a tailored training session for this course, please reach out to us.

Smart Robotics involves integrating artificial intelligence into robotic systems to enhance perception, decision-making, and autonomous control capabilities.

This instructor-led live training, available online or onsite, targets advanced robotics engineers, systems integrators, and automation leads looking to implement AI-driven perception, planning, and control within smart manufacturing settings.

Upon completing this training, participants will be equipped to:

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

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live laboratory environment.

Course Customization Options

• For customized training requests, please contact us to arrange details.