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

Artificial Intelligence (AI) for Robotics integrates machine learning, control systems, and sensor fusion to develop intelligent machines capable of autonomous perception, reasoning, and action. By leveraging modern tools such as ROS 2, TensorFlow, and OpenCV, engineers can now create 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 looking to develop, train, and deploy AI-driven robotic systems using contemporary 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 behaviours.
• Implement Kalman and Particle Filters for accurate localization and tracking.
• Apply computer vision techniques via OpenCV for perception and object detection.
• Employ TensorFlow for motion prediction and learning-based control.
• Integrate SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Develop reinforcement learning models to enhance robotic decision-making capabilities.

Course Format

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

Course Customisation Options

To request a customised training session for this course, please get in touch to arrange it.

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.

Course Format

• 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, functions as a digital assistant designed to automate user interactions across various messaging platforms, enabling faster task completion without requiring direct human intervention.

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

Upon completing this training, participants will be able to:

• Grasp the diverse uses and applications of bots
• Comprehend the entire bot development lifecycle
• Explore the various tools and platforms utilized in bot construction
• Construct a sample chatbot for Facebook Messenger
• Construct a sample chatbot using the Microsoft Bot Framework

Audience

• Developers seeking to create their own bots

Format of the course

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

Edge AI allows artificial intelligence models to execute directly on embedded or resource-constrained devices, thereby reducing latency and power consumption while enhancing autonomy and privacy within robotic systems.

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

• Comprehend the core principles 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.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice using TinyML and edge AI toolchains.
• Practical exercises on embedded and robotic hardware platforms.

Course Customization Options

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

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course aimed at introducing participants to the design and implementation of intuitive interfaces for human–robot communication. This training blends theory, design principles, and programming practice to help learners build natural and responsive interaction systems using speech, gestures, and shared control techniques. Participants will acquire skills in integrating perception modules, developing multimodal input systems, and designing robots that collaborate safely with humans.

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

Upon completion 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 the gap between industrial automation and contemporary robotics frameworks. Participants will acquire the skills to integrate ROS-based robotic systems with PLCs for synchronized operations, whilst exploring digital twin environments to simulate, monitor, and optimise production processes. The curriculum places a strong emphasis on interoperability, real-time control, and predictive analysis through the use of digital replicas of physical systems.

This instructor-led live training, available both online and onsite, targets intermediate-level professionals seeking to develop practical expertise in linking ROS-controlled robots with PLC environments and implementing digital twins to optimise automation and manufacturing outcomes.

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

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

Course Format

• Interactive lectures and architecture walkthroughs.
• Practical 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.

This instructor-led live training in Portugal (offered online or onsite) is designed for engineers seeking to understand the application of artificial intelligence to mechatronic systems.

Upon completing this training, participants will be equipped to:

• Obtain a comprehensive overview of artificial intelligence, machine learning, and computational intelligence.
• Comprehend the fundamental concepts of neural networks and various learning methodologies.
• Select appropriate artificial intelligence strategies to address real-world challenges effectively.
• Develop and implement AI solutions within the field of mechatronic engineering.

Multi-Robot Systems and Swarm Intelligence is an advanced training course that explores 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 Portugal (online or onsite) is designed for 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.

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

• Implement multimodal sensing within robotic systems.
• Develop AI algorithms for sensor fusion and decision-making.
• Build robots capable of executing complex tasks in dynamic environments.
• Tackle challenges related to real-time data processing and actuation.

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

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

• Grasp 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 practical course designed to assist developers in efficiently building, testing, and deploying robotic applications. Participants will learn to containerise robotics environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker to ensure reproducibility and scalability. The course places emphasis on agility, version control, and collaboration practices that are well-suited for early-stage development and innovation teams.

This instructor-led live training (available online or onsite) targets beginner to intermediate-level participants who wish to accelerate their robotics development workflows using ROS 2 and Docker.

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

• Set up a ROS 2 development environment within Docker containers.
• Develop and test robotic prototypes within modular, reproducible setups.
• Utilise simulation tools to validate system behaviour prior to hardware deployment.
• Collaborate effectively through containerised robotics projects.
• Apply continuous integration and deployment concepts within robotics pipelines.

Course Format

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

Course Customisation Options

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

Reinforcement learning (RL) represents a machine learning paradigm where agents acquire decision-making skills by interacting with their environment. Within the field of robotics, RL empowers autonomous systems to cultivate adaptive control and decision-making capabilities through experience and feedback.

This instructor-led training, available online or on-site, 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 equipped to:

• Grasp the principles and mathematical foundations of reinforcement learning.
• Implement RL algorithms including Q-learning, DDPG, and PPO.
• Integrate RL with robotic simulation environments utilizing OpenAI Gym and ROS 2.
• Train robots to execute complex tasks autonomously via 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

• For customized training requests for this course, please get in touch to make arrangements.

Deep Learning for Robotic Manipulation and Grasping is an advanced programme that connects robotic control with contemporary machine learning methodologies. Participants will investigate how deep learning can improve perception, motion planning, and dexterous grasping within robotic systems. Through theoretical instruction, simulation, and practical coding activities, the course leads learners from perception-based control towards end-to-end policy learning for manipulation tasks.

This instructor-led, live training (available online or onsite) targets advanced-level professionals who wish to apply deep learning techniques to enable intelligent, adaptable, and precise robotic manipulation.

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

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

Course Format

• Expert-led lectures and detailed algorithmic analysis.
• Practical coding and simulation exercises.
• Project-based implementation and testing.

Course Customization Options

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

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

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

Upon completing 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.
• Extensive 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.