Multimodal AI in Robotics Training Course

The intersection of Artificial Intelligence (AI) and robotics integrates machine learning, control systems, and sensor fusion to engineer intelligent machines capable of autonomous perception, reasoning, and action. Leveraging contemporary frameworks such as ROS 2, TensorFlow, and OpenCV, engineers are empowered to design robotic solutions that navigate, plan, and interact with physical environments with sophistication.

This instructor-led live training, available either online or onsite, targets intermediate-level engineers seeking to develop, train, and deploy AI-driven robotic systems using state-of-the-art open-source technologies.

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

• Utilize Python and ROS 2 to construct and simulate robotic behaviors.
• Deploy Kalman and Particle Filters for precise localization and tracking.
• Apply OpenCV-based computer vision techniques 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 paired with group discussions.
• Practical implementation exercises using ROS 2 and Python.
• Hands-on practice within both simulated and real-world robotic environments.

Course Customization Options

For inquiries regarding customized training sessions for this course, please contact our team to make arrangements.

In this instructor-led, live training in the UAE (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.

ROS 2 (Robot Operating System 2) serves as an open-source framework engineered to facilitate the creation of complex and scalable robotic applications.

This instructor-led live training, available either online or onsite, is designed for intermediate-level robotics engineers and developers aiming to implement autonomous navigation and SLAM (Simultaneous Localization and Mapping) using ROS 2.

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

• Setting up and configuring ROS 2 for autonomous navigation applications.
• Implementing SLAM algorithms to facilitate mapping and localization.
• Integrating sensors, including LiDAR and cameras, with ROS 2.
• Simulating and testing autonomous navigation within the Gazebo environment.
• Deploying 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 request 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, functions as a virtual assistant designed to automate user interactions across various messaging platforms. This enables faster task completion without requiring direct communication with a human agent.

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

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

• Understand the diverse uses and applications of bots
• Comprehend the end-to-end process of bot development
• Explore the various tools and platforms utilized in building bots
• Create a sample chatbot for Facebook Messenger
• Develop a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bots

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-limited devices, thereby lowering latency and power usage while boosting autonomy and privacy within robotic systems.

This instructor-led live training (available online or onsite) targets intermediate-level embedded developers and robotics engineers seeking 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.

Format of the Course

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

Course Customization Options

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

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

Upon completing this training, participants will be equipped to:

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

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course focused on the design and implementation of intuitive interfaces for effective human–robot communication. This training integrates theoretical knowledge, design principles, and programming practice to create natural and responsive interaction systems leveraging speech, gestures, and shared control techniques. Participants will acquire the skills to integrate perception modules, develop multimodal input systems, and design robots that safely collaborate with humans.

Delivered by an instructor either online or onsite, this training targets participants at a beginner to intermediate level who aim to design and implement human–robot interaction systems that improve usability, safety, and overall user experience.

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

• Grasping the foundational concepts and design principles of human–robot interaction.
• Developing voice-based control and response mechanisms for robots.
• Implementing gesture recognition utilizing computer vision techniques.
• Designing collaborative control systems that ensure safe and shared autonomy.
• Evaluating 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 conducted 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, hands-on course designed to bridge the gap between industrial automation and modern robotic frameworks. Participants will acquire the skills to integrate 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.

This instructor-led training, available both online and onsite, targets intermediate-level professionals seeking to build practical expertise 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 able to:

• Comprehend the communication protocols used between ROS and PLC systems.
• Implement real-time data exchange mechanisms between robots and industrial controllers.
• Develop digital twins for monitoring, testing, and process simulation.
• Integrate sensors, actuators, and robotic manipulators into industrial workflows.
• Design and validate industrial automation systems using hybrid simulation environments.

Format of the Course

• Interactive lectures and architectural walkthroughs.
• Hands-on exercises focused on integrating ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Course Customization Options

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

This instructor-led live training in the UAE (online or onsite) is tailored for engineers interested in learning how artificial intelligence applies 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.

The advanced training course on Multi-Robot Systems and Swarm Intelligence delves into the design, coordination, and control of robotic teams, drawing inspiration from biological swarm behaviors. Participants will gain insights into modeling interactions, implementing distributed decision-making processes, and optimizing collaboration across multiple agents. The curriculum blends theoretical knowledge with practical simulation exercises, preparing learners for applications in logistics, defense, search and rescue, and autonomous exploration.

This live, instructor-led training, available both online and onsite, targets advanced professionals aiming 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 equipped to:

• Grasp the principles and dynamics of swarm intelligence and cooperative robotics.
• Develop communication and coordination strategies for multi-robot systems.
• Implement distributed decision-making and consensus algorithms.
• Simulate collective behaviors, including formation control, flocking, and coverage.
• Apply swarm-based techniques to real-world scenarios and optimization challenges.

Course Format

• Advanced lectures featuring algorithmic deep dives.
• Practical coding and simulation exercises in ROS 2 and Gazebo.
• Collaborative project focusing on the application of swarm intelligence principles.

Course Customization Options

• For customized training arrangements, please reach out to us.

This instructor-led, live training delivered in the UAE (online or onsite) is designed for intermediate-level participants seeking to improve their skills in designing, programming, and deploying intelligent robotic systems for automation and other applications.

By the conclusion 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 assist developers in building, testing, and deploying robotic applications with efficiency. Participants will acquire the skills to containerize robotics environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker to ensure reproducibility and scalability. The curriculum places strong emphasis on agility, version control, and collaborative practices tailored for early-stage development and innovation teams.

This instructor-led, live training (available online or onsite) targets beginner to intermediate-level participants looking to accelerate their robotics development workflows through the use of ROS 2 and Docker.

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

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

Course Format

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

Course Customization Options

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

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

This instructor-led live training, available either online or onsite, targets advanced-level machine learning engineers, robotics researchers, and developers aiming to design, implement, and deploy reinforcement learning algorithms for robotic applications.

Upon completing this training, participants will be equipped to:

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

Course Format

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

Customization Options

• To request a customized version of this course, please get in touch with us to arrange your session.

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

This instructor-led training, available online or onsite, is designed for advanced robotics engineers, systems integrators, and automation leads who want to implement AI-driven perception, planning, and control within smart manufacturing environments.

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

• Understand and apply AI techniques for robotic perception and sensor fusion.
• Develop motion planning algorithms for both 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.
• Numerous exercises and practice opportunities.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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