robotic

As preparations begin for the pilot launch in South Africa, Kenya, Côte d’Ivoire, Ghana, and Nigeria, this two-hour orientation and awareness session provides a key introduction for educators to the AI and Robotics Youth Training Programme and its specialized Training of Trainers methodology.
The session presents a high-impact learning model built around an equal balance of theory and practice. Through this approach, teachers are guided across the full engineering journey, beginning with foundational AI literacy and advancing toward the design and construction of an autonomous robot to address predefined problem statements. Structured through intensive eight-hour training sessions, the programme gradually equips educators with the knowledge, tools, and confidence needed to support students in developing solutions for a prototype challenge aligned with the ITU Robotics for Good Youth Challenge.
By connecting technical learning with real-world problem-solving, the session seeks to ensure that educators are strategically prepared to transform their schools into spaces of sustained creativity, experimentation, and innovation. Drawing on Giga’s connectivity, Google’s technical expertise, and the research leadership of IHFC-IIT Delhi, the programme aims to empower a new generation of young African innovators and problem-solvers.

Institutions

• AI for Good

Future robots need to be robust and adaptable, and new design approaches are needed for new production methods. I will talk about my research in using evolutionary algorithms and biologically inspired methods with the aim of having more intelligent, robust, and adaptive behavior in robots. I will give a short introduction to some of the algorithms and show how we, at the University of Oslo, apply them in our research platforms for exploring automatic robot design and adaptation. Here, we take an embodied AI approach and aim to co-design the body and the behavior of the robots, such that they are well fit for their intended environments and tasks. These approaches are still at a fundamental research stage, and I will discuss potential future application areas, as well as challenges and opportunities related to the sustainability of these AI systems.

Learning objectives:

By the end of the session, participants will be able to:

• List some bio-inspired AI approaches for robot design and adaptation.
• Discuss challenges and opportunities related to algorithmic robot design.

Recommended mastery level:

• Some undergraduate level engineering or computer science skills would be beneficial, although not absolutely necessary.

Network of Labs presents: Die sechste Digital Science Night – eine Abendveranstaltung zu einem Thema aus der weiten Welt der Computer, Algorithmen und künstlichen Intelligenzen. Mit Science Slams und einer interaktiven Ausstellung – dieses mal rund um die Frage von Robotern und Agentic AI in der Arbeitswelt.

Zeitplan

18:00–01:00 Robotic Work – Ausstellung
18:30–18:45 Kollege Roboter: Der Einzug humanoider Roboter in Arbeitswelt und Alltag
19:00–20:00 Robotic Work – Science Slam (Teil 1)
21:30–21:45 Kollege Roboter: Der Einzug humanoider Roboter in Arbeitswelt und Alltag
22:00–23:00 Robotic Work – Science Slam (Teil 2)

Robotic Work – Science Slam

Roboter nehmen uns die Arbeit ab – oder weg? Agentic AI und humanoide Roboter finden sich überall im Arbeitsleben. In der Pflege, im High-Speed-Trading oder bei der Rettungsarbeit. In einem Science Slam ringen sechs Slammer*innen auf offener Bühne um die beste Erklärung für ihre wissenschaftliche Themen rund um die Arbeitswelt.

Ayse Glass, Frederic Voigt, Hassan Said, Ahmad Khalidi, Juan Hernandez, Vera Schorbach und weitere zu Themen

Institution

• Network of Labs
• Projekts “Leuphana AI Campus”

Artificial Intelligence is transforming how we approach chemical research and synthesis. By teaching language models to understand and generate the language of chemistry, we have developed complementary AI systems that bridge the gap between computational design and experimental reality.

Our large language model system, ChemCrow, represents one of the first demonstrations of an AI system directly controlling robotic synthesis platforms, successfully executing the synthesis of compounds including organocatalysts and chromophores.

Complementing this, our small language model system, Saturn, currently the most sample-efficient molecular design algorithm, enables precise molecular generation with built-in synthesizability constraints. Saturn’s innovations include direct optimization against retrosynthetic predictions and integration of building block availability, ensuring that generated molecules are practically accessible.

Our work demonstrates how different scales of language models can work together to transform chemical research, from initial molecular design through to physical synthesis, potentially revolutionizing drug discovery, catalysis, and materials development.