AI for Robotics Training in Gaia

This practical, instructor-led training in Gaia focuses on agile prototyping for robotics using ROS 2 and Docker. Participants will gain hands-on expertise in building, testing, and deploying modular robotic systems, with a particular focus on containerisation, simulation, and version control to support scalable development.

This practical HRI course in Gaia explores voice, gesture, and collaborative control. It teaches how to build intuitive, multimodal interfaces for secure human-robot collaboration, with a focus on applied design principles, speech recognition, gesture detection, and shared autonomy to boost usability and safety.

This hands-on training in Gaia addresses ROS-PLC integration and digital twins within industrial automation. Participants will learn to synchronize robotic systems with PLCs, create digital replicas for simulation purposes, and enhance production workflows through real-time data exchange and advanced monitoring techniques.

A specialist training in Gaia focused on deep learning for robotic manipulation. Gain expertise in perception, grasp planning, and control using neural networks and ROS 2. Practical simulation exercises equip you to implement intelligent and adaptive grasping solutions on real robotic arms.

An advanced training course in Gaia that focuses on the design and coordination of multi-robot systems. Learners will gain the skills to model swarm intelligence, implement distributed decision-making, and optimize agent collaboration through hands-on simulations using ROS 2 and Gazebo, preparing them for real-world operational applications.

This instructor-led, live training in Gaia guides advanced professionals in integrating TinyML into autonomous robotics. Participants will learn to design optimised models, implement on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.

This 21-hour, instructor-led training in Gaia focuses on safety cases, verification, and explainable AI within the context of robotics. It guides participants in applying hazard analysis, ethical governance, and compliance standards to ensure transparent and trustworthy robotic decision-making in real-world scenarios.

This live training in Gaia equips intermediate engineers with the skills to deploy TinyML models for robotics. Participants will learn to optimize inference for speed and energy efficiency, integrate AI into control systems, and develop autonomous, low-latency robotic solutions directly on embedded hardware.

This instructor-led, live training in Gaia (online or on-site) is designed for intermediate learners interested in exploring the function of collaborative robots (cobots) and other human-centric AI systems within modern workplaces.

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

• Comprehend the fundamental principles of Human-Centric Physical AI and its real-world applications.
• Analyse the role collaborative robots play in improving workplace productivity.
• Detect and resolve challenges related to human-machine interaction.
• Create workflows that enhance collaboration between humans and AI-driven systems.
• Encourage a culture of innovation and adaptability in workplaces utilising AI.

This instructor-led live training in Gaia delves into reinforcement learning principles and deep RL techniques such as PPO and DDPG. Participants will implement algorithms using Python and PyTorch, integrate them with ROS 2 and OpenAI Gym, and train autonomous robots to resolve complex tasks.

This instructor-led training in Gaia explores computer vision for robotics by combining OpenCV with deep learning. Participants will master the implementation of perception pipelines, the integration of detection models, and the deployment of systems on embedded platforms for autonomous control.

This instructor-led, live training in Gaia (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.

In Gaia, this live training equips advanced engineers with the AI skills necessary for robotic perception, planning, and control. Participants will apply sensor fusion, develop motion algorithms, and deploy learning-based strategies to effectively integrate intelligent systems into smart manufacturing workflows.

This instructor-led live training in Gaia provides intermediate robotics engineers with practical skills in autonomous navigation and SLAM using ROS 2. Learners will gain experience integrating LiDAR sensors, implementing mapping algorithms, simulating scenarios in Gazebo, and deploying real-world navigation stacks on physical robots.

This instructor-led, live training in Gaia (available online or onsite) is tailored for intermediate-level professionals looking to advance their capabilities in designing, programming, and deploying intelligent robotic systems for automation and extended use cases.

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

• Comprehend the fundamental principles of Physical AI and its role in robotics and automation.
• Design and program intelligent robotic systems suited for dynamic operational environments.
• Integrate AI models to enable autonomous decision-making within robots.
• Employ simulation tools for effective robotic testing and performance optimisation.
• Resolve technical challenges related to sensor fusion, real-time processing, and energy efficiency.

This instructor-led live training in Gaia (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.