Learning FAIR

Robots are beginning to appear in public spaces and shaping our cities, from delivery systems on sidewalks to machines that inspect infrastructure. Today, designing and programming robots typically requires specialist expertise. Learning FAIR aims to make those capabilities accessible to a much wider audience.

As robots become part of everyday urban life, more people need the knowledge and skills to understand how they work and influence how they are used.

Learning FAIR combines education with hands-on experimentation and collaboration, giving people the tools and confidence to explore robotics, develop their own ideas and actively shape how intelligent machines are used in their cities. 

Learning FAIR (Learning Foundry for AI & Robotics) is an international, open-source initiative aimed at making urban robotics accessible to people without advanced technical expertise. Led by the MIT Media Lab, the initiative brings together the MIT Media Lab’s City Science group, City Science Lab @ Taipei Tech, Stuttgart Media University, and non-academic partners to support its development, impact, and long-term sustainability.

Start with a city problem. Not a programming class.

Participants start with a tangible local challenge, such as urban heat, dry green spaces, accessibility barriers or litter. They create a visual robot workflow, test it in simulation and transfer it to a physical robot. Every technical step stays tied to a problem in their own city.

Open by design

The platform, learning resources and robot workflows will be openly available, so that other cities can reuse and adapt them. One city's solution can become every city's starting point.

Visit our website: www.learning-fair.org