Much of the groundbreaking advancements we've witnessed over the past decade, spanning from computer vision, speech recognition, protein folding prediction, and beyond, hinge on the deep learning work conducted by Geoffrey Hinton, who has fundamentally changed the focus and

Our newest update demonstrates the Scaling Law in action, with RFM-1 now generating images at 400% higher resolution than previously.

This higher resolution leads to more accurate AI predictions and fewer hallucinations. Check out RFM-1's enhanced ability to predict how a scene

the more data the better it works

Leveraging collective knowledge from every Covariant Brain-powered robot, fulfillment centers all over the world are gaining an edge with Covariant AI Robotics.

What data is necessary for general robotic intelligence?

Peter Chen's perspective from his panel at NVIDIA GTC: covariant.ai/insights/found…

Robotics is about more than just lines of code! 💾

It's about the real, physical machines that can navigate our world.

It’s the real, physical objects that robot needs to deal with, in real environments, often shared with humans.

Covariant recently released RFM-1.

ROBOTICS

'There’s a well-known observation among roboticists: What is hard for humans is easy for machines, and what is easy for humans is hard for machines.' - Melissa Heikkilä, MIT Technology Review

This dynamic is changing: technologyreview.com/2024/04/11/109…

In a landmark year for robotics, we're forging new approaches to how robots engage and operate in the real world.

Explore our open positions and join us in shaping the future of robotics: covariant.ai/careers

Large language models are trained to predict the next token in text. For robotics, this means training models on physical interaction datasets to build generalized AI that can simulate the physical world.

We've built RFM-1, the first commercial Robotics Foundation Model. RFM-1

Where innovation meets efficiency.

Dive into The Robots: covariant.ai/insights/the-r…

A multimodal any-to-any sequence model, RFM-1 is an 8B parameter transformer trained on text, images, videos, robot actions, numerical and sensor data.

RFM-1 tokenizes all modalities into a common space and performs autoregressive next-token prediction with a broad range of

resonates with my own experience:

robot demo --> INSANE PAIN --> robot creating value in production

luckily I am hopeful we have by now paid most of our dues at Covariant :)

Dive in to RFM-1's latest scaling update: In-context learning. covariant.ai/insights/rfm-1…

Building a truly general AI for robotics must be done in the real world. To make this happen, our focus lies in addressing critical issues and delivering true value to our customers.

During the recent #NvidiaGTC2024 , Peter Chen highlighted how generative AI is reshaping the

A robot is a tool -- hopefully a very powerful one -- and every job needs the right tool

“We believe there will be a huge breadth of robotic form factors. Different types of hardware for different use cases— this all depends on frequency, speed, cost of installation, etc.' - Peter Chen at NVIDIA GTC

'I just realized I am the only person on the stage that is not a CTO' - Peter Chen, Covariant CEO at NVIDIA GTC