Safe & Explainable Robotics: Verification, Safety Cases & Ethics 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.

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.

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

Edge AI allows artificial intelligence models to operate directly on embedded or resource-limited devices, thereby lowering latency and power usage 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 who aim to implement machine learning inference and optimization techniques directly on robotic hardware using TinyML and edge AI frameworks.

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

• Grasp the fundamentals 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.

Course Format

• Interactive lectures and discussions.
• Hands-on practice utilizing TinyML and edge AI toolchains.
• Practical exercises on embedded and robotic hardware platforms.

Customization Options

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

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.

Practical Rapid Prototyping for Robotics with ROS 2 & Docker is a hands-on course designed to help developers build, test, and deploy robotic applications efficiently. Participants will learn how to containerize robotics environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker for reproducibility and scalability. The course emphasizes agility, version control, and collaboration practices suitable for early-stage development and innovation teams.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level participants who wish to accelerate 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 containerized robotics projects.
• Apply continuous integration and deployment concepts in robotics pipelines.

Format of the Course

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

Course Customisation Options

• To request a customised training 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.
• Optimize 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 Customization Options

• To request a customized training 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 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.