Tesla's robots, both wheeled and legged, are developed with the goal of achieving general-purpose capability, analogous to the versatility observed in humans and animals. These systems rely primarily on scalable sensing modalities such as vision, audio etc, enabling robust performance within stringent power and cost constraints.

This talk will describe the principles and methodology behind constructing foundation models for robotics at Tesla. We will discuss the architecture, data and training of large-scale multimodal models that control these robots in an end-to-end pixels-to-actuation fashion. We will also examine evaluation protocols, safety considerations, and strategies for reliable real-world deployment. Finally, we project the transformational benefits to society that widespread deployment of such advanced robotic systems can deliver.