Frontier models for robot dexterity
Our frontier models deliver precise, smooth, high-frequency, human-like control from a single recipe. With no task-specific engineering, we use the same approach for every use case.
To overcome the practical limitations of robotics data we combine data across qualities and quantities, from large-scale video to scalable wearable data collection and high-quality data on the real robot. By matching our robot embodiment to the human hand, we hold the morphology constant across every phase of learning.
01
Video pre-training
We leverage large-scale video data, image, language, cross-embodiment robot data during pre-training. This enables fast downstream generalization with minimal adaptation overhead.
02
Wearable data
We pioneer wearable human data collection methods at scale to quickly gather rich datasets. This allows rapid scaling to new customer use cases, making the human hand our strongest asset.
03
Fine-tuning & post-training
With an efficient fine-tuning recipe we craft policies for real-world deployments, not just demos. Closing the feedback loop with reinforcement learning post-training we optimize for high success rates in long-term operations
