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

The convergence of Artificial Intelligence (AI) and Robotics leverages machine learning, control systems, and sensor fusion to engineer intelligent machines that can perceive, reason, and act autonomously. By utilizing modern frameworks such as ROS 2, TensorFlow, and OpenCV, engineers are now equipped to design robotic systems that intelligently navigate, plan, and interact with complex real-world environments.

This instructor-led live training, available both online and onsite, is designed for intermediate-level engineers seeking to develop, train, and deploy AI-driven robotic systems using contemporary open-source technologies.

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

• Building and simulating robotic behaviors using Python and ROS 2.
• Implementing Kalman and Particle Filters for precise localization and tracking.
• Applying computer vision techniques via OpenCV for perception and object detection.
• Utilizing TensorFlow for motion prediction and learning-based control mechanisms.
• Integrating SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Developing reinforcement learning models to enhance robotic decision-making capabilities.

Format of the Course

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

Course Customization Options

To arrange a customized training session for this course, please get in touch with 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 serves as a powerful open-source library for computer vision, facilitating real-time image processing, while deep learning frameworks like TensorFlow equip robotic systems with the capabilities needed for intelligent perception and decision-making.

This instructor-led training, available online or on-site, is tailored for robotics engineers, computer vision specialists, and machine learning practitioners at an intermediate level. The program focuses on applying computer vision and deep learning methodologies to enhance robotic perception and autonomy.

Upon completing this training, participants will be equipped to:

• Build computer vision pipelines utilizing OpenCV.
• Incorporate deep learning models for effective object detection and recognition.
• Leverage visual data to guide robotic control and navigation.
• Synergize traditional vision algorithms with deep neural networks.
• Deploy computer vision solutions onto embedded devices and robotic platforms.

Training Format

• Engaging lectures paired with interactive discussions.
• Practical exercises using OpenCV and TensorFlow.
• Live lab sessions involving simulated or physical robotic systems.

Customization Options

• For tailored training arrangements, please reach out to us.

A bot, or chatbot, acts as a digital assistant designed to automate user interactions across various messaging platforms, enabling faster task completion without requiring direct human contact.

In this instructor-led live training, participants will gain foundational skills in bot development by building sample chatbots using industry-standard tools and frameworks.

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

• Identify the diverse applications and use cases of bots
• Comprehend the end-to-end bot development lifecycle
• Evaluate various tools and platforms suitable for bot construction
• Construct a sample chatbot for Facebook Messenger
• Develop a sample chatbot utilizing the Microsoft Bot Framework

Target Audience

• Developers eager to create their own bot solutions

Course Format

• A blend of lectures, group discussions, practical exercises, and extensive hands-on practice

Edge AI allows artificial intelligence models to execute directly on embedded or resource-constrained devices, thereby minimizing 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 eager to implement machine learning inference and optimization techniques directly on robotic hardware using TinyML and edge AI frameworks.

Upon completing this training, participants will be capable of:

• Gaining a solid understanding of TinyML and edge AI fundamentals for robotics.
• Converting and deploying AI models for on-device inference.
• Optimizing models to improve speed, reduce size, and enhance energy efficiency.
• Integrating edge AI systems into robotic control architectures.
• Evaluating performance and accuracy in real-world scenarios.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Kenya (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:

• 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.

Industrial Robotics Automation: ROS-PLC Integration & 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 PLCs for synchronized operations, while 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 through the use of digital replicas of physical systems.

This instructor-led live training, available either online or onsite, targets intermediate-level professionals seeking to develop practical competencies in linking ROS-controlled robots with PLC environments and implementing digital twins for automation and manufacturing optimization.

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

• Comprehend the communication protocols facilitating interaction between ROS and PLC systems.
• Execute real-time data exchange mechanisms between robots and industrial controllers.
• Create 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.

Course Format

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

Customization Options for the Course

• To request a customized training session for this course, please get in touch with us to make arrangements.

This instructor-led live training in Kenya (online or onsite) is designed for engineers who want to explore how artificial intelligence can be applied to mechatronic systems.

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

• Obtain an overview of artificial intelligence, machine learning, and computational intelligence.
• Grasp the concepts of neural networks and various learning methods.
• Select the most effective AI approaches for addressing real-world problems.
• Implement AI applications within mechatronic engineering.

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 behaviors. Participants will learn how to model interactions, implement distributed decision-making, and optimize collaboration across multiple agents. The course combines theory with hands-on simulation to prepare learners for applications in logistics, defense, 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 behaviors such as formation control, flocking, and coverage.
• Apply swarm-based techniques to real-world scenarios and optimization 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 Customization Options

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

This instructor-led, live training in Kenya (online or onsite) is designed for advanced robotics engineers and AI researchers eager to harness Multimodal AI to integrate diverse sensory data, thereby creating robots that are more autonomous, efficient, and capable of seeing, hearing, and touching.

Upon completing this training, participants will be equipped to:

• Implement multimodal sensing within robotic systems.
• Develop AI algorithms for sensor fusion and decision-making processes.
• Build robots capable of executing complex tasks in dynamic environments.
• Tackle challenges associated with real-time data processing and actuation.

This instructor-led, live training in Kenya (online or onsite) is designed for intermediate-level participants looking to strengthen their skills in designing, programming, and deploying intelligent robotic systems for automation and other applications.

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

• Grasp the core principles of Physical AI and its uses in robotics and automation.
• Design and program intelligent robotic systems for dynamic environments.
• Implement AI models that enable autonomous decision-making in robots.
• Utilize simulation tools for robotic testing and optimization.
• Address challenges like sensor fusion, real-time processing, and energy efficiency.

Hands-On Rapid Prototyping for Robotics Using ROS 2 & Docker is a practical course crafted to assist developers in efficiently building, testing, and deploying robotic applications. Participants will acquire skills in containerizing robotics environments, integrating ROS 2 packages, and prototyping modular robotic systems by leveraging Docker for enhanced reproducibility and scalability. The curriculum places strong emphasis on agility, version control, and collaborative practices ideal for innovation teams at the early stages of development.

This instructor-led live training, available both online and onsite, is tailored for participants at beginner to intermediate levels who aim to speed up their robotics development workflows through the use of ROS 2 and Docker.

Upon completing this training, participants will be capable of:

• Establishing a ROS 2 development environment within Docker containers.
• Developing and testing robotic prototypes within modular and reproducible setups.
• Utilizing simulation tools to validate system behaviour prior to hardware deployment.
• Collaborating effectively via containerized robotics projects.
• Applying continuous integration and deployment concepts within robotics pipelines.

Course Format

• Interactive lectures and demonstrations.
• Practical exercises involving ROS 2 and Docker environments.
• Mini-projects centred on real-world robotic applications.

Customization Options

• To request a customized training session for this course, please reach out to us to make arrangements.

Reinforcement learning (RL) is a machine learning approach where agents acquire decision-making skills by engaging with their environment. In the context of robotics, RL empowers autonomous systems to build adaptive control and decision-making abilities through experiential learning and feedback.

This instructor-led live training, available online or on-site, targets advanced machine learning engineers, robotics researchers, and developers who aim to design, implement, and deploy reinforcement learning algorithms within robotic applications.

Upon completing this training, participants will be capable of:

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

Course Format

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

Customization Options

• To arrange a tailored training session for this course, please contact us directly.

Deep Learning for Robotic Manipulation and Grasping is an advanced program that integrates robotic control with contemporary machine learning methods. Participants will investigate how deep learning can improve perception, motion planning, and dexterous grasping within robotic systems. Through theoretical foundations, simulations, and practical coding exercises, the course directs learners from perception-based control towards end-to-end policy learning for manipulation tasks.

This instructor-led live training (available online or onsite) targets advanced-level professionals eager to apply deep learning techniques to achieve intelligent, adaptable, and precise robotic manipulation.

Upon completing this training, participants will be able to:

• Create 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 optimize 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 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 capabilities, and autonomous control.

This instructor-led live training, available either online or onsite, is designed for advanced robotics engineers, systems integrators, and automation leads who aim to implement AI-driven perception, planning, and control within smart manufacturing settings.

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

• Grasp and apply AI techniques for robotic perception and sensor fusion.
• Create motion planning algorithms for both collaborative and industrial robots.
• Implement learning-based control strategies to facilitate real-time decision-making.
• Seamlessly integrate intelligent robotic systems into smart factory workflows.

Course Format

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

Course Customization Options

• To request tailored training for this course, please reach out to us to make the necessary arrangements.