Accelerating the robot design process is achieved by shifting the burden from developing software controls to creating high-fidelity simulations. The term "high-fidelity" holds different meanings for various tasks. For certain tasks, a highly detailed and realistic simulation is essential for accurately assessing robot performance. On the other hand, some tasks might only require basic representations of the environment and interactions. 

 

We envision training a foundational model capable of fine-tuning for specific robotics tasks, which would then be used to predict the immediate outcomes of events based on intricate knowledge of the possibilities. Full future distribution. For example, the model could anticipate the effectiveness of grout application, the likelihood of a nail breaking under the robot's force, potential damage to objects upon contact, or the breaking point of aquatic weeds during removal.

 

To ensure the model's predictions closely align with reality, it is crucial to collect data from a large fleet of robots that interact with the diverse, real-world industrial environment, as opposed to being confined to predefined spaces like car manufacturing robots. While companies like Google and Facebook AI have developed unbounded robots, they do not have thousands-strong fleets for large-scale data collection and evaluation.