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

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

This instructor-led, live training (available online or onsite) is designed for intermediate-level engineers who wish 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 behaviors.
• Implement Kalman and Particle Filters for localization 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 Localization and Mapping) for autonomous navigation.
• Develop reinforcement learning models to improve robotic decision-making.

Format of the Course

• Interactive lecture and discussion.
• Hands-on implementation using ROS 2 and Python.
• Practical exercises with simulated and real robotic environments.

Course Customization Options

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

This instructor-led live training, delivered Botswana (online or onsite), enables participants to learn the various technologies, frameworks, and techniques for programming different types of robots for use in nuclear technology and environmental systems.

The six-week course is conducted five days a week. Each day consists of four hours of lectures, discussions, and hands-on robot development in a live lab setting. Participants will complete real-world projects applicable to their work to practice their acquired knowledge.

The target hardware for this course will be simulated in 3D via simulation software. The open-source ROS (Robot Operating System) framework, along with 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 (available online or on-site) 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.

Course Format

• 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 integrates the features of the Microsoft Bot Framework and Azure Functions, offering a robust platform for rapidly constructing intelligent bots.

In this instructor-led live training, attendees will discover how to efficiently develop intelligent bots using Microsoft Azure.

Upon completion of the training, attendees will be able to:

Grasp the fundamental concepts underpinning intelligent bots.

Construct intelligent bots leveraging cloud-based applications.

Acquire practical expertise in the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Implement recognised bot design patterns in practical scenarios.

Create and deploy their inaugural 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, functions as a digital assistant designed to automate user interactions across various messaging platforms. This enables tasks to be completed more efficiently without the necessity of human interaction.

During this instructor-led live training, participants will learn how to begin developing a bot by working through the creation of sample chatbots using bot development tools and frameworks.

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

• Grasp the various uses and applications of bots
• Understand the end-to-end process of developing bots
• Explore the different tools and platforms utilised in building bots
• Construct a sample chatbot for Facebook Messenger
• Construct a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bot

Course Format

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

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

Upon completing this training, participants will be able to:

• Grasp the principles of Human-Centric Physical AI and their practical applications.
• Examine how collaborative robots enhance workplace productivity.
• Identify and resolve challenges associated with human-machine interactions.
• Design workflows that optimise collaboration between humans and AI-driven systems.
• Foster a culture of innovation and adaptability in AI-integrated workplaces.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course designed to acquaint participants with the design and implementation of intuitive interfaces for human–robot communication. The training blends 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 Customization Options

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

The course 'Industrial Robotics Automation: ROS-PLC Integration & Digital Twins' is a practical programme designed to bridge the gap between industrial automation and contemporary robotics frameworks. Participants will acquire the skills to integrate ROS-based robotic systems with PLCs for synchronized operations, while also exploring digital twin environments to simulate, monitor, and optimise production processes. The training places a strong emphasis on interoperability, real-time control, and predictive analysis through the use of digital replicas of physical systems.

This instructor-led live training is available both online and onsite, targeting intermediate-level professionals who wish to develop practical expertise in linking ROS-controlled robots with PLC environments and implementing digital twins for automation and manufacturing optimisation.

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

• Grasp the communication protocols used 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.

Course Format

• Interactive lectures and architecture walkthroughs.
• Practical exercises focusing on the integration of ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Customisation Options

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

This instructor-led, live training in Botswana (online or onsite) is designed for engineers who wish to learn about 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.

The Multi-Robot Systems and Swarm Intelligence course is an advanced programme that investigates the design, coordination, and control of robotic teams, drawing inspiration from biological swarm dynamics. Participants will acquire the skills to model interactions, execute distributed decision-making processes, and optimise collaboration among multiple agents. By blending theoretical foundations with practical simulation, the course prepares learners for applications in logistics, defence, search and rescue operations, and autonomous exploration.

This instructor-led, live training session (available online or onsite) targets advanced-level professionals aiming 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 equipped to:

• Grasp the principles and dynamics of swarm intelligence and cooperative robotics.
• Formulate communication and coordination strategies for multi-robot networks.
• Deploy 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 challenges.

Course Format

• Advanced lectures featuring in-depth algorithmic analysis.
• Practical coding and simulation exercises using ROS 2 and Gazebo.
• A collaborative project applying swarm intelligence principles.

Course Customisation Options

• To request a bespoke training session for this course, please contact us to make arrangements.

This instructor-led, live training in Botswana (online or onsite) targets advanced 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 Botswana (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:

• Understand the principles of Physical AI and its applications in robotics and automation.
• Design and program intelligent robotic systems for dynamic environments.
• Implement AI models for autonomous decision-making in robots.
• Leverage simulation tools for robotic testing and optimization.
• Address challenges such as sensor fusion, real-time processing, and energy efficiency.

A Smart Robot is an Artificial Intelligence (AI) system that learns from its surroundings and experiences, enhancing its capabilities based on that acquired knowledge. These intelligent machines can work alongside humans, observing and learning from their behaviour. Beyond performing physical labour, they are also equipped to handle complex cognitive tasks. In addition to mechanical units, Smart Robots can be entirely software-based, existing as applications within a computer system without any physical components or direct interaction with the physical world.

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

The course is structured into four sections, with each section spanning three days of lectures, discussions, and practical robot development in a live lab environment. Each section concludes with a practical hands-on project, allowing participants to practice and demonstrate their newly acquired skills.

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

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
• Comprehend and implement the software components that form the basis of Smart Robots
• Construct 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 blend 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 make arrangements.

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

This instructor-led training (available online or onsite) is designed for advanced-level robotics engineers, systems integrators, and automation leads who wish to implement AI-driven perception, planning, and control in 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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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