Covariant
Canvas Category Machinery : Industrial Robot : Piece Picking
Since the first industrial robots were introduced in the 1960s, millions have been deployed globally. Their impact is undeniable. Robots have automated countless dangerous, repetitive tasks, transforming manufacturing, but they’ve only reached a fraction of their potential. Incapable of thinking on their own, they can only do pre-programmed tasks in tightly-controlled environments. They can’t understand, learn, or adapt.Building on our experience at Berkeley and OpenAI, our vision is the Covariant Brain: universal AI that allows robots to see, reason, and act on the world around them. We’re bringing the Covariant Brain to commercial viability, starting with the industries that make, move and store things in the physical world.
Assembly Line
Covariant Announces a Universal AI Platform for Robots
Covariant is announcing RFM-1, which the company describes as a robotics foundation model that gives robots the “human-like ability to reason.” “Foundation model” means that RFM-1 can be trained on more data to do more things—at the moment, it’s all about warehouse manipulation because that’s what it’s been trained on, but its capabilities can be expanded by feeding it more data. “Our existing system is already good enough to do very fast, very variable pick and place,” says Covariant co-founder Pieter Abbeel. “But we’re now taking it quite a bit further. Any task, any embodiment—that’s the long-term vision. Robotics foundation models powering billions of robots across the world.” From the sound of things, Covariant’s business of deploying a large fleet of warehouse automation robots was the fastest way for them to collect the tens of millions of trajectories (how a robot moves during a task) that they needed to train the 8 billion parameter RFM-1 model.
The Future of Robotics: Robotics Foundation Models and the Role of Data
One key factor that has enabled the success of generative AI in the digital world is a foundation model trained on a tremendous amount of internet-scale data. However, a comparable dataset did not previously exist in the physical world to train a robotics foundation model. That dataset had to be built from the ground up — composed of vast amounts of “warehouse-scale” real-world data and synthetic data.
Covariant’s robotics foundation model relies on this mix of data. General image data, paired with text data, is used to help the model learn a foundational semantic understanding of the visual world. Real-world warehouse production data, augmented with simulation data, is used to refine its understanding of specific tasks needed in warehouse operations, such as object identification, 3D understanding, grasp prediction, and place prediction.
AI-automated meal kitting powered by Covariant
How generalized AI outperforms specialized models
Our universal AI, the Covariant Brain, powering ABB and Fabuc robots simultaneously
Covariant raises $75M for its AI-powered warehouse robots
Covariant, a startup developing warehouse robots that can be deployed faster than traditional automation hardware, has raised $75 million in funding. The company disclosed the investment today. The capital was provided as an extension to a Series C round that it had originally announced in 2021. Radical Ventures and Index Ventures co-led the new investment, while five other institutional backers contributed as well.
Radial Selects Covariant to Automate eCommerce Fulfillment with AI-Powered Robotics
Radial, Inc., a bpost group company, the leader in eCommerce solutions, today announced a new partnership with Covariant, a leading global AI Robotics company, to automate sortation in their batch-picking operations through the installation of twelve Covariant Robotic Putwalls. The Robotic Putwalls sort a high variety of health and beauty items for one of the world’s leading retailers in Radial’s fulfillment center in Louisville, Kentucky.
The integration of Covariant’s Robotic Putwalls into the existing facility operations delivers more optimal eCommerce fulfillment performance and accuracy – providing high quality, reliable and consistent customer experience for Radial’s clients. With a more automated order sortation system, Radial can reduce worker strain and fill gaps in its workforce, while improving overall facility output and delivery times, specifically around high-demand periods.
Covariant Robotic Depalletization | Mixed-SKU Pallets
Covariant Robotic Induction Sizzle Reel
KNAPP and Covariant Partnership Advances AI Robotics for more Efficient Warehouses
KNAPP, technology partner for intelligent value chains, and Covariant, a leading global AI robotics company, strengthen their partnership to further develop AI-powered robot solutions and expand their market presence. So far, KNAPP and Covariant have successfully implemented multiple projects together, with KNAPP’s picking robot – the Pick-it-Easy Robot – at the forefront of their endeavors. The robot’s ability to handle a wide range of items and suitability for various sector applications have proven their value in improving warehouse efficiency across numerous industries.
Robotic Flexibility: How Today’s Autonomous Systems Can Be Adapted to Support Changing Operational Needs
While robots are ideally suited to repetitive tasks, until now they lacked the intelligence to identify and handle tens of thousands of constantly changing products in a typical dynamic warehouse operation. That made applying robots to picking applications somewhat limited. Therefore, when German electrical supply wholesaler Obeta sought to install a new automated storage system from MHI member KNAPP in its new Berlin warehouse as a means to address a regional labor shortage made worse by COVID-19, the company specified a robotic picking system powered by onboard artificial intelligence (AI).
“The Covariant Brain is a universal AI that allows robots to see, reason and act in the world around them, completing tasks too complex and varied for traditional programmed robots. Covariant’s software enables Obeta’s Pick-It-Easy Robot to adapt to new tasks on its own through trial and error, so it can handle almost any object,” explained Peter Chen, co-founder and CEO of MHI member Covariant.ai.