Practical Rapid Prototyping for Robotics with ROS 2 & Docker Training Course

Artificial Intelligence (AI) for Robotics merges machine learning, control systems, and sensor fusion to engineer intelligent machines capable of autonomous perception, reasoning, and action. Leveraging contemporary tools such as ROS 2, TensorFlow, and OpenCV, engineers can now develop robots that navigate, plan, and interact with real-world environments in an intelligent manner.

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 the latest open-source technologies and frameworks.

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

• Utilize Python and ROS 2 to construct and simulate robotic behaviors.
• Implement Kalman and Particle Filters for localization and tracking purposes.
• Apply computer vision techniques via OpenCV for perception and object detection.
• Employ TensorFlow for motion prediction and learning-based control mechanisms.
• Integrate SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Develop reinforcement learning models to enhance robotic decision-making capabilities.

Course Format

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

Customization Options

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

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, an open-source library for computer vision, facilitates real-time image processing, while deep learning frameworks like TensorFlow equip robotic systems with the capabilities for intelligent perception and decision-making.

This instructor-led training, available both online and onsite, is designed for robotics engineers, computer vision specialists, and machine learning engineers at an intermediate level who aim to leverage computer vision and deep learning techniques to enhance robotic perception and autonomy.

Upon completing this training, participants will be capable of:

• Building computer vision pipelines using OpenCV.
• Incorporating deep learning models for object detection and recognition.
• Utilizing vision-based data for robotic control and navigation.
• Merging classical vision algorithms with deep neural networks.
• Deploying computer vision solutions on robotic and embedded platforms.

Course Format

• Interactive lectures and discussions.
• Practical exercises using OpenCV and TensorFlow.
• Live-lab implementation on physical or simulated robotic systems.

Course Customization Options

• To arrange customized training for this course, please contact us directly.

A bot, or chatbot, acts as a digital assistant designed to automate user interactions across various messaging platforms, enabling tasks to be completed more swiftly without requiring human intervention.

In this instructor-led live training, participants will gain hands-on experience in bot development by building sample chatbots using established development tools and frameworks.

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

• Comprehend the diverse uses and applications of bots
• Grasp the end-to-end process of bot development
• Explore the array of tools and platforms available for constructing bots
• Develop a sample chatbot for Facebook Messenger
• Construct a sample chatbot leveraging the Microsoft Bot Framework

Audience

• Developers keen on creating their own bot solutions

Format of the course

• A blend of lectures, discussions, exercises, and intensive hands-on practice

Edge AI allows artificial intelligence models to operate directly on embedded or resource-constrained devices, thereby minimising latency and power usage while enhancing autonomy and privacy within robotic systems.

This instructor-led live training, available both online and onsite, targets intermediate-level embedded developers and robotics engineers seeking to apply machine learning inference and optimisation techniques directly onto robotic hardware via TinyML and edge AI frameworks.

Upon completing this training, participants will be equipped to:

• Grasp the core principles of TinyML and edge AI for robotics.
• Convert and deploy AI models for on-device inference.
• Optimise models for improved speed, compactness, and energy efficiency.
• Integrate edge AI systems into robotic control architectures.
• Assess performance and accuracy in real-world scenarios.

Format of the Course

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

Course Customization Options

• For those seeking a tailored version of this course, please reach out to us to arrange it.

This instructor-led live training in Malaysia (online or onsite) is designed for 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:

• 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.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course aimed at introducing participants to the design and implementation of intuitive interfaces for human–robot communication. The training blends theoretical concepts, design principles, and programming practice to construct natural and responsive interaction systems utilising speech, gesture, and shared control techniques. Participants will gain insights into integrating perception modules, developing multimodal input systems, and designing robots that collaborate safely with humans.

This instructor-led, live training (available online or onsite) is tailored for beginner to intermediate-level participants keen on designing and implementing human–robot interaction systems that enhance usability, safety, and user experience.

Upon completing this training, participants will be able to:

• Grasp the fundamentals 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 modern robotics frameworks. Participants will acquire the skills to integrate ROS-based robotic systems with Programmable Logic Controllers (PLCs) for synchronized operations, while also exploring digital twin environments to simulate, monitor, and optimize production processes. The curriculum places a strong emphasis on interoperability, real-time control, and predictive analysis by utilizing digital replicas of physical systems.

This instructor-led, live training, available online or on-site, is tailored for intermediate-level professionals seeking to develop practical expertise in connecting ROS-controlled robots with PLC environments and implementing digital twins to enhance automation and manufacturing efficiency.

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

• Understand the communication protocols governing interaction between ROS and PLC systems.
• Implement real-time data exchange mechanisms between robots and industrial controllers.
• Develop digital twins for monitoring, testing, and simulating processes.
• Integrate sensors, actuators, and robotic manipulators into industrial workflows.
• Design and validate industrial automation systems using hybrid simulation environments.

Format of the Course

• Interactive lectures and architecture walkthroughs.
• Hands-on exercises focused on integrating ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Course Customization Options

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

This instructor-led live training in Malaysia (available online or onsite) is aimed at engineers who wish to understand the applicability of artificial intelligence to mechatronic systems.

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

• Gain an overview of artificial intelligence, machine learning, and computational intelligence.
• Understand the concepts of neural networks and different learning methods.
• Choose artificial intelligence approaches effectively for real-life problems.
• Implement AI applications in mechatronic engineering.

Multi-Robot Systems and Swarm Intelligence is an advanced training programme that delves into the design, coordination, and control of robotic teams, drawing inspiration from biological swarm behaviours. Participants will gain insights into modelling interactions, implementing distributed decision-making processes, and optimising collaboration across multiple agents. The course integrates theoretical concepts with practical simulation exercises to equip learners with the skills needed for applications in logistics, defence, search and rescue operations, and autonomous exploration.

This instructor-led, live training session (available online or onsite) is tailored for advanced-level professionals who aim to design, simulate, and deploy multi-robot and swarm-based systems using open-source frameworks and algorithms.

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

• Grasp 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 optimization challenges.

Course Format

• Advanced lectures featuring in-depth algorithmic analysis.
• Practical coding and simulation work in ROS 2 and Gazebo.
• Collaborative project applying swarm intelligence principles.

Course Customization Options

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

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

• Grasp the core principles of Physical AI and its uses in robotics and automation.
• Design and code intelligent robotic systems for dynamic settings.
• Apply AI models to enable autonomous decision-making in robots.
• Utilise simulation tools to test and optimise robotic performance.
• Tackle challenges like sensor fusion, real-time processing, and energy efficiency.

Reinforcement learning (RL) represents a machine learning approach in which agents acquire decision-making skills by engaging with their surrounding environment. Within the field of robotics, RL empowers autonomous systems to cultivate adaptive control and decision-making abilities by leveraging experience and feedback.

This live training session, facilitated by an instructor and available either online or onsite, is designed for advanced machine learning engineers, robotics researchers, and developers who aim to design, implement, and deploy reinforcement learning algorithms within robotic applications.

Upon completion of this training, participants will be capable of:

• Gaining a solid understanding of reinforcement learning principles and underlying mathematics.
• Implementing RL algorithms including Q-learning, DDPG, and PPO.
• Integrating RL with robotic simulation environments through the use of OpenAI Gym and ROS 2.
• Training robots to execute complex tasks autonomously via trial and error mechanisms.
• Optimising training performance by utilizing deep learning frameworks such as PyTorch.

Course Format

• Interactive lectures and discussions.
• Practical implementation exercises using Python, PyTorch, and OpenAI Gym.
• Hands-on practice in both simulated and physical robotic environments.

Customisation Options

• For those wishing to request a tailored training version of this course, please get in touch with us to make arrangements.

This advanced course integrates robotic control principles with contemporary machine learning methodologies. Participants will investigate how deep learning can improve perception, motion planning, and dexterous grasping capabilities within robotic systems. Through a blend of theoretical instruction, simulation, and practical coding assignments, the curriculum guides learners from perception-based control mechanisms to end-to-end policy learning for complex manipulation tasks.

This instructor-led live training, available both online and onsite, targets advanced professionals seeking to leverage deep learning methods to achieve intelligent, adaptable, and precise robotic manipulation.

Upon completion of this training, participants will gain the ability to:

• Construct perception models for object recognition and pose estimation.
• Train neural networks designed for grasp detection and motion planning.
• Integrate deep learning modules with robotic controllers via ROS 2.
• Simulate and evaluate grasping and manipulation strategies in virtual environments.
• Deploy and optimize learned models on either real or simulated robotic arms.

Course Format

• Lectures led by experts with in-depth exploration of algorithms.
• Practical coding and simulation exercises.
• Implementation and testing through project-based learning.

Customization Options

• To request a customized training program for this course, please get in touch with us to arrange the details.

Smart Robotics involves the integration of artificial intelligence into robotic systems to enhance perception, decision-making capabilities, and autonomous control.

This instructor-led live training, available either online or onsite, 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.

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

• Understand and apply AI techniques for robotic perception and sensor fusion.
• Develop motion planning algorithms suitable for both collaborative and industrial robots.
• Deploy learning-based control strategies to facilitate real-time decision making.
• Integrate intelligent robotic systems into smart factory workflows.

Format of the Course

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

Course Customization Options

• To request customized training for this course, please contact us to arrange the details.