Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control 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.

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.

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

Edge AI facilitates the execution of artificial intelligence models directly on embedded or resource-limited devices. This approach minimises latency and power usage while enhancing both autonomy and privacy within robotic systems.

This instructor-led live training, available online or onsite, targets intermediate embedded developers and robotics engineers seeking to implement machine learning inference and optimisation techniques directly on robotic hardware using TinyML and edge AI frameworks.

Upon completing this training, participants will be able to:

• Grasp the core principles of TinyML and edge AI as applied to robotics.
• Convert and deploy AI models for inference directly on devices.
• Optimise models for improved speed, reduced size, and greater energy efficiency.
• Integrate edge AI systems into robotic control architectures.
• Evaluate performance and accuracy in real-world scenarios.

Format of the Course

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

Course Customization 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) 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.

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.

Practical Rapid Prototyping for Robotics with ROS 2 & Docker is a hands-on course designed to assist developers in efficiently building, testing, and deploying robotic applications. Participants will learn to containerise robotics environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker to ensure reproducibility and scalability. The course places a strong emphasis on agility, version control, and collaborative practices that are well-suited for early-stage development and innovation teams.

This instructor-led, live training (available online or onsite) is aimed at beginner to intermediate 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 containerised 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) constitutes a machine learning approach in which agents acquire decision-making skills by interacting with their surroundings. Within the field of robotics, RL empowers autonomous systems to cultivate adaptive control and decision-making faculties through direct experience and feedback mechanisms.

This live, instructor-led training (available online or on-site) is tailored for advanced-level machine learning engineers, robotics researchers, and developers who aspire to design, implement, and deploy reinforcement learning algorithms within robotic applications.

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

• Gaining a solid grasp of reinforcement learning principles and mathematics.
• Implementing RL algorithms such as Q-learning, DDPG, and PPO.
• Integrating RL with robotic simulation environments using OpenAI Gym and ROS 2.
• Training robots to execute complex tasks autonomously via trial and error.
• Enhancing training performance by leveraging deep learning frameworks like PyTorch.

Course Format

• Interactive lectures and discussions.
• Practical implementation exercises using Python, PyTorch, and OpenAI Gym.
• Hands-on tasks within simulated or physical robotic environments.

Customisation Options

• To arrange customised training for this course, please reach out to us.

Robotic Manipulation and Grasping Using Deep Learning is an advanced programme that connects robotic control with contemporary machine learning methodologies. Participants will investigate how deep learning can improve perception, motion planning, and dexterous grasping within robotic systems. Through theoretical concepts, simulation, and practical coding activities, the course guides learners from perception-based control towards end-to-end policy learning for manipulation tasks.

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

By the conclusion of this training, participants will be capable of:

• Developing perception models for object recognition and pose estimation.
• Training neural networks for grasp detection and motion planning.
• Integrating deep learning modules with robotic controllers using ROS 2.
• Simulating and evaluating grasping and manipulation strategies in virtual environments.
• Deploying and optimising learned models on real or simulated robotic arms.

Course Format

• Expert-led lectures and algorithmic deep dives.
• Hands-on coding and simulation exercises.
• Project-based implementation and testing.

Course Customisation Options

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

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.