Edge AI for Robots: TinyML, On-Device Inference & Optimization Training Course

Artificial Intelligence (AI) for Robotics integrates machine learning, control systems, and sensor fusion to develop intelligent machines capable of autonomously perceiving, reasoning, and acting. 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 online or onsite) is designed for intermediate-level engineers seeking to develop, train, and deploy AI-driven robotic systems using contemporary open-source technologies and frameworks.

Upon completing this training, participants will be able to:

• Utilise Python and ROS 2 to construct and simulate robotic behaviours.
• Implement Kalman and Particle Filters for precise localization and tracking.
• Apply computer vision techniques via OpenCV for perception and object detection.
• Use TensorFlow for motion prediction and learning-based control.
• Integrate SLAM (Simultaneous Localisation 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 within simulated and real robotic environments.

Course Customisation Options

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

In this instructor-led live training in Australia (available online or on-site), participants will learn the diverse technologies, frameworks, and techniques for programming various types of robots for use in the field of nuclear technology and environmental systems.

The six-week course is held five days a week. Each day lasts four hours 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) 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.

Azure Bot Service unites the power of the Microsoft Bot Framework with Azure Functions, offering a robust platform for rapidly creating intelligent bots.

In this instructor-led, live training, participants will discover efficient methods for developing intelligent bots using Microsoft Azure.

Upon completing the training, participants will be capable of:

Grasping the fundamental concepts behind intelligent bots.

Constructing intelligent bots using cloud-based applications.

Acquiring practical knowledge of the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Applying established bot design patterns to real-world scenarios.

Creating and deploying their first intelligent bot using Microsoft Azure.

Target Audience

This course is tailored for developers, hobbyists, engineers, and IT professionals keen on bot development.

Course Format

The training blends lectures and discussions with exercises, placing a strong emphasis on hands-on practice.

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

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

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

• Understand the various use cases and applications of bots
• Comprehend the end-to-end process of developing bots
• Explore the range of tools and platforms available for bot development
• Build a sample chatbot for Facebook Messenger
• Build a sample chatbot using the 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

This instructor-led, live training in Australia (online or onsite) is designed for intermediate-level participants keen to investigate the role of collaborative robots (cobots) and other human-centric AI systems within contemporary workplaces.

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

• Grasp the core principles of Human-Centric Physical AI and its practical applications.
• Explore how collaborative robots contribute to enhancing workplace productivity.
• Identify and address challenges arising from human-machine interactions.
• Design workflows that optimise collaboration between humans and AI-driven systems.
• Foster a culture of innovation and adaptability in workplaces integrated with AI.

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

This instructor-led, live training (available online or onsite) targets 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: Integrating ROS with PLCs and Digital Twins is a practical course designed to bridge the gap between industrial automation and contemporary robotics frameworks. Participants will learn how to integrate ROS-based robotic systems with PLCs for synchronized operations, while exploring digital twin environments to simulate, monitor, and optimise production processes. The course places a strong emphasis on interoperability, real-time control, and predictive analysis using digital replicas of physical systems.

This instructor-led, live training (available online or onsite) is aimed at intermediate-level professionals who wish to develop practical skills in connecting ROS-controlled robots with PLC environments and implementing digital twins for automation and manufacturing optimisation.

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 lectures and architecture walkthroughs.
• Hands-on exercises integrating ROS and PLC systems.
• Simulation and digital twin project implementation.

Course Customisation Options

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

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.

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 behaviours. Participants will learn how to model interactions, implement distributed decision-making, and optimise collaboration across multiple agents. The course blends theory with practical simulation to prepare learners for applications in logistics, defence, 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 behaviours such as formation control, flocking, and coverage.
• Apply swarm-based techniques to real-world scenarios and optimisation 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 Customisation Options

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

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 (online or on-site) targets intermediate-level participants looking to sharpen their abilities in designing, programming, and deploying intelligent robotic systems for automation and related fields.

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

• Grasp the principles of Physical AI and its applications in robotics and automation.
• Design and programme intelligent robotic systems for dynamic environments.
• Implement AI models to enable autonomous decision-making in robots.
• Utilise simulation tools for robotic testing and optimisation.
• Tackle challenges such as sensor fusion, real-time processing, and energy efficiency.

A Smart Robot is an Artificial Intelligence (AI) system capable of learning from its environment and past experiences, thereby enhancing its capabilities based on the knowledge gained. These robots can collaborate with humans, working alongside them and learning from their behaviour. Moreover, they are capable of performing not only manual labour but also cognitive tasks. Beyond physical hardware, Smart Robots can exist purely as software-based applications on computers, operating without moving parts or direct physical interaction with the world.

In this instructor-led live training, participants will explore the various technologies, frameworks, and techniques required to programme different types of mechanical Smart Robots. They will then apply this knowledge to complete their own Smart Robot projects.

The course is structured into 4 sections, each comprising three days of lectures, discussions, and hands-on robot development within a live laboratory environment. Each section concludes with a practical, hands-on project, allowing participants to practise and demonstrate their acquired knowledge.

The target hardware for this course will be simulated in 3D using simulation software. The ROS (Robot Operating System) open-source framework, along with C++ and Python, will be utilised for programming the robots.

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

• Grasp the key concepts underpinning robotic technologies
• Understand and manage the interaction between software and hardware in a robotic system
• Understand and implement the software components that form the foundation of Smart Robots
• Build and operate a simulated mechanical Smart Robot capable of seeing, sensing, processing, grasping, navigating, and interacting with humans via voice
• Enhance a Smart Robot's ability to perform complex tasks through Deep Learning
• Test and troubleshoot a Smart Robot in realistic scenarios

Audience

• Developers
• Engineers

Format of the course

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

Note

• To customise any aspect of this course (such as programming language or robot model), please contact us to arrange.

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

This instructor-led live training (available online or on-site) is designed for advanced-level robotics engineers, systems integrators, and automation leads seeking 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.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical practice.
• Hands-on implementation in a live lab environment.

Course Customisation Options

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