A new wave of AI development is increasingly relying on real-world human behaviour, with DoorDash moving to tap its gig workforce to generate training data for robotics systems.
DoorDash has launched a standalone app called Tasks, allowing couriers to earn money by recording themselves performing everyday activities such as folding clothes, washing dishes or making a bed. The collected data is used to train AI and robotics models to understand physical environments and human interactions better.
The move reflects a broader shift in AI training, where companies are seeking physical, real-world data rather than relying solely on text and images. Such data is essential for building systems capable of performing tasks in dynamic environments, including humanoid robots and autonomous machines.
Other companies are pursuing similar strategies. Uber and Instawork have tested gig-based data-collection models, while robotics startups are using wearable devices, such as gloves and head-mounted cameras, to capture detailed motion data for training.
The Tasks app is currently being rolled out as a pilot, with DoorDash planning to expand the types of available assignments over time. Some tasks may also be integrated into the main Dasher app, including activities that support navigation or assist autonomous delivery systems.
As competition intensifies, access to large-scale physical data is becoming a critical advantage. DoorDash’s approach highlights how gig-economy platforms are increasingly integrated into the development of next-generation AI systems.
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