Robotics Startups Lean on Foundation Models to Cut Task-Specific Training Time

## Starting From Scratch Was the Old Default Training a robot to perform a new task traditionally meant building a control policy from the ground up for that specific task, requiring extensive task-specific data collection and training time before a robot could perform reliably. ## Fine-Tuning Instead of Training From Zero Robotics foundation models trained on broad, general physical-interaction data give startups a strong starting point that can be fine-tuned for a specific task with a much smaller task-specific dataset, cutting time-to-deployment substantially. ## The Competitive Effect This is lowering the capital and time required to bring a new robotics product to market, letting smaller startups compete on task-specific execution rather than needing to have solved general physical manipulation from scratch themselves.