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

Artificial Intelligence (AI) for Robotics brings together machine learning, control systems, and sensor fusion to create intelligent machines capable of perceiving, reasoning, and acting autonomously. With modern tools such as ROS 2, TensorFlow, and OpenCV, engineers can now design robots that navigate, plan, and interact intelligently with real-world environments.

This instructor-led, live training (available online or on-site) is designed for intermediate-level engineers who want to develop, train, and deploy AI-driven robotic systems using current open-source technologies and frameworks.

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

• Use Python and ROS 2 to build and simulate robotic behaviours.
• Implement Kalman and Particle Filters for localisation and tracking.
• Apply computer vision techniques using 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.

Course Format

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

Course Customisation Options

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

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 Localisation 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 localisation.
• 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 Customisation Options

• To request a customised 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 Customisation Options

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

A bot, or chatbot, is like a digital assistant designed to automate user interactions across various messaging platforms, enabling tasks to be completed faster without requiring human intervention.

In this instructor-led, live training, participants will learn how to begin developing their own bots by creating sample chatbots using industry-standard bot development tools and frameworks.

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

• Understand the diverse uses and applications of bots
• Grasp the end-to-end process of bot development
• Explore the range of tools and platforms used in building bots
• Build a sample chatbot for Facebook Messenger
• Build a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bots

Course Format

• A blend of lecture, discussion, hands-on exercises, and practical application

This instructor-led, live training in New Zealand (online or onsite) is aimed at intermediate-level participants who wish to explore 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:

• Grip 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.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a hands-on course designed to introduce participants to the design and implementation of intuitive interfaces for human–robot communication. The training combines theory, design principles, and programming practice to build natural and responsive interaction systems using speech, gesture, and shared control techniques. Participants will learn how to integrate perception modules, develop multimodal input systems, and design robots that safely collaborate with humans.

This instructor-led, live training (online or onsite) is aimed at 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 Customisation Options

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

Industrial Robotics Automation: ROS-PLC Integration & Digital Twins is a hands-on course focused on bridging industrial automation with modern robotics frameworks. Participants will learn to integrate ROS-based robotic systems with PLCs for synchronized operations and explore digital twin environments to simulate, monitor, and optimise production processes. The course emphasises interoperability, real-time control, and predictive analysis using digital replicas of physical systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to build 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 lecture 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 New Zealand (available online or on-site) is designed for engineers who wish to explore how artificial intelligence can be applied to mechatronic systems.

By the end 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.
• Select appropriate artificial intelligence approaches to effectively address real-world problems.
• Implement AI-driven applications within mechatronic engineering contexts.

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 behaviours. Participants will learn how to model interactions, implement distributed decision-making, and optimise collaboration across multiple agents. The course combines theory with hands-on simulation to prepare learners for applications in logistics, defence, search and rescue, and autonomous exploration.

This instructor-led, live training (online or on-site) 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 New Zealand (available online or onsite) is designed for advanced-level robotics engineers and AI researchers who wish to leverage Multimodal AI to integrate diverse sensory data, creating 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 within robotic systems.
• Develop AI algorithms for sensor fusion and decision-making.
• Create robots capable of performing complex tasks in dynamic environments.
• Tackle challenges related to real-time data processing and actuation.

This instructor-led, live training in New Zealand (available online or on-site) is designed for intermediate-level participants seeking to enhance their skills in designing, programming, and deploying intelligent robotic systems for automation and beyond.

By the end 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 tailored for dynamic environments.
• Implement AI models to enable autonomous decision-making in robots.
• Leverage simulation tools for robotic testing and performance optimisation.
• Address key 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 greater efficiency. Participants will learn how to containerise robotics environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker to ensure reproducibility and scalability. The course 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 on-site) is aimed at beginner to intermediate-level participants who wish to accelerate their robotics development workflows using ROS 2 and Docker.

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

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

Format of the Course

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

Course Customisation Options

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

Reinforcement learning (RL) is a machine learning paradigm where agents learn to make decisions by interacting with an environment. In robotics, RL enables autonomous systems to develop adaptive control and decision-making capabilities through experience and feedback.

This instructor-led, live training (online or onsite) is aimed at advanced-level machine learning engineers, robotics researchers, and developers who wish to design, implement, and deploy reinforcement learning algorithms in robotic applications.

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

• Understand the principles and mathematics of reinforcement learning.
• Implement RL algorithms such as Q-learning, DDPG, and PPO.
• Integrate RL with robotic simulation environments using OpenAI Gym and ROS 2.
• Train robots to perform complex tasks autonomously through trial and error.
• Optimise training performance using deep learning frameworks like PyTorch.

Format of the Course

• Interactive lecture and discussion.
• Hands-on implementation using Python, PyTorch, and OpenAI Gym.
• Practical exercises in simulated or physical robotic environments.

Course Customisation Options

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

Robot Manipulation and Grasping with Deep Learning is an advanced course that bridges robotic control with modern machine learning techniques. Participants will explore how deep learning can enhance perception, motion planning, and dexterous grasping in robotic systems. Through theory, simulation, and practical coding exercises, the course guides learners from perception-based control to end-to-end policy learning for manipulation tasks.

This instructor-led, live training (online or on-site) is aimed at advanced-level professionals who wish to apply deep learning methods to enable intelligent, adaptable, and precise robotic manipulation.

By the end 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 optimise learned models on real or simulated robotic arms.

Course Format

• Expert-led lectures and in-depth algorithmic explorations.
• Hands-on coding and simulation exercises.
• Project-based implementation and testing.

Course Customisation Options

• To request a customised training session for this course, 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 who aim to implement AI-driven perception, planning, and control within smart manufacturing environments.

By the end of 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.
• Abundant exercises and practical sessions.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

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