TinyML for Autonomous Systems and Robotics Training Course

This instructor-led, live training in Botswana (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.

Upon completing this training, participants will be able to:

• Grasp the principles of Human-Centric Physical AI and its practical applications.
• Examine how collaborative robots contribute to enhancing workplace efficiency.
• Recognise and tackle challenges arising from human-machine interactions.
• Develop workflows that optimise collaboration between humans and AI-driven systems.
• Foster a culture of innovation and adaptability within AI-integrated work environments.

This hands-on HRI course in Botswana focuses on voice, gesture, and collaborative control. Participants will learn to create intuitive, multimodal interfaces that promote safe human-robot collaboration, with an emphasis on practical design, speech recognition, gesture detection, and shared autonomy to optimize usability and safety.

This practical training in Botswana delves into ROS-PLC integration and digital twins within the realm of industrial automation. It guides learners in synchronizing robotic systems with PLCs, creating digital replicas for simulation purposes, and optimizing production workflows through real-time data exchange and advanced monitoring techniques.

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.

This advanced training in Botswana focuses on the design and coordination of multi-robot systems. Learners will acquire the ability to model swarm intelligence, implement distributed decision-making, and enhance collaboration across agents through hands-on simulation using ROS 2 and Gazebo, targeting practical real-world applications.

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, available online or onsite, targets intermediate-level participants looking to strengthen their expertise in designing, programming, and deploying intelligent robotic systems for automation and beyond.

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

• Comprehend the core principles of Physical AI and its diverse applications across robotics and automation sectors.
• Architect and program intelligent robotic systems capable of operating effectively in dynamic environments.
• Integrate AI models to facilitate autonomous decision-making within robotic frameworks.
• Utilize simulation tools to enhance robotic testing protocols and performance optimization.
• Navigate complex challenges including sensor fusion, real-time data processing, and energy efficiency.

This practical, instructor-led training in Botswana focuses on rapid robotics prototyping via ROS 2 and Docker. Participants will gain hands-on proficiency in building, testing, and deploying modular robotic systems, learning essential skills in containerization, simulation, and version control to support scalable development efforts.