Dyna Robotics Unveils DYNA-2 Robot Foundation Model

August 10, 2026
Dyna Robotics announced DYNA-2, a robot foundation model trained on more than 1 million hours of egocentric human video. The company says the model improves task success rates and can adapt to several robot platforms with limited local training data.
Dyna Robotics Unveils DYNA-2 Robot Foundation Model

Dyna Robotics announced in a press release DYNA-2, a robot foundation model trained on more than 1 million hours of egocentric human video. The company said the model uses video data rather than manually collected robot action data to train robots for physical tasks.

DYNA-2 uses a dual next frame and next action world modeling architecture. Dyna Robotics said the model can transfer learning across stationary robot arms, humanoid prototypes, and dexterous robotic hands.

In manufacturing tasks cited by the company, DYNA-2 raised task success rates from 20% to 80% to 90% through larger pre training scale, without changes to post training data. Across 15 benchmark tasks, the company said policies trained with more human video data performed better.

Dyna Robotics also compared DYNA-2 with DYNA-1 in physical evaluations using matched training steps and datasets. The company said DYNA-2 completed tasks 1.55 times more often, reached an 87% pass rate at one customer deployment compared with 46% for DYNA-1, and used 13 minutes of local data to command a pair of five fingered robot hands to open a bottle cap.

We hope you enjoyed this article.

Consider subscribing to one of our newsletters like Robotics Brief or Daily AI Brief.

Also, consider following us on social media:

Subscribe to Robotics Brief

Weekly coverage of AI-driven robotics advances in industrial automation, autonomous vehicles, and robotic systems.

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more