The primary challenge in robotics AI is the lack of real-world training data. To solve this, models are bootstrapped using a combination of learning from human lifestyle videos and extensive simulation environments. This creates a foundational model capable of initial deployment, which then generates a real-world data flywheel.

To build generalist robots, the most effective approach is pre-training foundation models on internet-scale video datasets, not just simulation or tele-operated data. This vast, diverse data provides a deep, implicit understanding of physics and object interaction that is impossible to replicate in controlled environments, enabling true generalization.

Robotics company OneX designs its robot hands to be biomechanically identical to human hands not for aesthetics, but for data transfer. This allows them to train models on vast amounts of existing human video, which then 'just works' on the robot, bypassing the need for extensive simulation or teleoperation data.

The Physical Intelligence thesis is that a foundation model learning from diverse data can achieve a "physical understanding" of the world, making it easier to adapt to new tasks than building single-purpose robots from scratch. Generality leverages broader data, which is ultimately a more scalable approach.

Adopting the true 'foundation model ethos' from LLMs is difficult for roboticists. It means a warehouse automation company should collect data from kitchen robots. This breadth, while seemingly unrelated, builds a generalist model that better handles the weird edge cases in the target domain than a narrowly trained specialist model.

Physical Intelligence demonstrated an emergent capability where its robotics model, after reaching a certain performance threshold, significantly improved by training on egocentric human video. This solves a major bottleneck by leveraging vast, existing video datasets instead of expensive, limited teleoperated data.

One X's core bet is that by making its humanoid robot physically similar to a human, it can train its models on the immense, pre-existing dataset of general human video (e.g., YouTube). This solves the Catch-22 of needing massive robotics data, positioning the human as the 'cross-embodiment' platform.

Generalist CEO Pete Florence provides a tier list for robotics training data. He ranks "lived experience of the physical world" as S-tier, emphasizing the irreplaceable value of high-quality, real-world data. In contrast, he rates synthetic data from world models as F-tier, suggesting it is far less effective.

Neurobotics posits that true physical AI requires more than just vision-language models; it needs a "nervous system" and reflexes. They advocate for training robots in physical "gyms" to collect embodied data, arguing that complex physical tasks cannot be learned solely by watching videos.