Autonomous Navigation & SLAM with ROS 2 Training Course

This instructor-led training in Australia focuses on computer vision for robotics, utilizing OpenCV and deep learning. Learners will master the implementation of perception pipelines, the integration of detection models, and the deployment of systems on embedded platforms for autonomous control.

This live training in Australia guides intermediate engineers in deploying TinyML models for robotics. Participants will learn to optimise 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 Australia (online or onsite) targets intermediate-level participants interested in exploring the role of collaborative robots (cobots) and other human-centric AI systems in modern workplaces.

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

• Understand the principles of Human-Centric Physical AI and its applications.
• Explore the role of collaborative robots in enhancing workplace productivity.
• Identify and address challenges in human-machine interactions.
• Design workflows that optimize collaboration between humans and AI-driven systems.
• Promote a culture of innovation and adaptability in AI-integrated workplaces.

This practical HRI course in Australia focuses on voice, gesture, and collaborative control. Learn to construct intuitive, multimodal interfaces for safe human-robot collaboration, with an emphasis on practical design principles, speech recognition, gesture detection, and shared autonomy to boost usability and safety.

This hands-on training in Australia covers ROS-PLC integration and digital twins for industrial automation. Learn to synchronise robotic systems with PLCs, develop digital replicas for simulation, and optimise production workflows through real-time data exchange and advanced monitoring techniques.

This instructor-led, live training in Australia (online or onsite) is designed for engineers looking to explore the application of artificial intelligence within mechatronic systems.

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

• Gain a comprehensive overview of artificial intelligence, machine learning, and computational intelligence.
• Understand the fundamental concepts of neural networks and various learning methodologies.
• Effectively select appropriate artificial intelligence approaches for solving real-world problems.
• Implement AI applications in the field of mechatronic engineering.

An advanced training in Australia focusing on the design and coordination of multi-robot systems. Participants will learn to model swarm intelligence, implement distributed decision-making, and optimise collaboration across agents through hands-on simulation in ROS 2 and Gazebo for real-world applications.

This instructor-led, live training in Australia (online or onsite) is aimed at advanced-level 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.

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 Australia, offered either online or on-site, is tailored for intermediate-level participants aiming to refine their skills in designing, programming, and deploying intelligent robotic systems for automation and beyond.

By the conclusion of this training, participants will have acquired the ability to:

• Understand the underlying principles of Physical AI and how they apply to robotics and automation.
• Design and program intelligent robotic systems suited for dynamic operational environments.
• Implement AI models to enable autonomous decision-making in robotic platforms.
• Leverage simulation tools for comprehensive robotic testing and optimisation.
• Navigate complex challenges such as sensor fusion, real-time processing, and energy efficiency.

This practical, instructor-led training in Australia focuses on rapid prototyping for robotics leveraging ROS 2 and Docker. Participants will acquire the skills to build, test, and deploy modular robotic systems with efficiency, gaining hands-on expertise in containerisation, simulation, and version control to support scalable development.

This live, instructor-led training in Australia delves into the principles of reinforcement learning 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 tackle complex tasks.

Advanced training in Australia focused on deep learning for robotic manipulation. Develop skills in perception, grasp planning, and control using neural networks and ROS 2. Practical simulation exercises prepare you to effectively deploy intelligent, adaptable grasping solutions on real robotic arms.