KNAPP and Covariant Extend Their Success Story

KNAPP and Covariant announce extension of their partnership

The automation expert KNAPP and Covariant, the world’s leading AI Robotics company based in California, are a perfect match when it comes to intelligent robotics solutions. Both companies are pleased to announce the extension of their multi-year partnership. Covariant, founded as a start-up in Silicon Valley, is a pioneer in the development of Robotic Foundation Models models that enable robots to operate in complex and dynamic environments. KNAPP and Covariant’s joint projects focus on the robot picking solution Pick-it-Easy Robot, which combines state-of-the-art technology and extensive logistics expertise and has already proven itself internationally in many customer applications. The market presence in AI-powered robot solutions will be expanded and the future looks promising.

Pioneers for robotics and AI in logistics applications

KNAPP began developing robotic solutions for single-piece handling and implementing them in automation solutions many years ago. With the AI and image processing system from Covariant, the robot solution Pick-it-Easy Robot powered by Covariant AI was raised to an all new level about 5 years ago. AI opened up new possibilities for using robotics in logistics applications by providing the sophistication needed to process items with different sizes, surface properties or packaging. The chemistry between KNAPP and Covariant was right from the start, both technologically and on a personal level. “We decided to run some tests together and challenge Covariant’s artificial intelligence. The results were very impressive. That’s why we decided to partner and combine our robot and logistics expertise with Covariant’s AI,” recalls Peter Puchwein, Vice President of Research & Development at KNAPP. Today, the Pick-it-Easy Robot is in use at a total of 26 KNAPP customers in various sectors in Europe, North America and Australia. These include projects with well-known companies such as Würth, McKesson and Brodrene Dahl.

The collaboration underlines the commitment both companies have made to technological excellence and provides an effective solution for the labor shortage. Ted Stinson, Covariant COO, emphasizes “This longstanding partnership exemplifies what happens when two pioneers join forces. We’re energized by the trust KNAPP has placed in Covariant and look forward to continuing to drive transformative results for our mutual customers, and the industry at large”.

Powerful support in numerous logistics processes

The Pick-it-Easy Robot is a combination of KNAPP’s KiSoft software and the image processing of the Covariant Brain. This allows the automatic handling of individual items, for example, for high-performance order picking or the fully automatic transfer of goods to pocket sorter systems. Pick-it-Easy Robot is already relieving warehouse employees of monotonous routine picking tasks in numerous applications. This allows staff to be deployed more effectively and tasks to be carried out more efficiently. Pick-it-Easy Robot provides support where workers are missing or only available to a limited extent, allowing order processing around the clock. Companies can thus optimize their personnel resources and, at the same time, increase the capacity of the warehouse without needing more staff.

The robot solution is suitable for greenfield applications and proven to succeed in brownfield environments.

A glimpse of the future

The Pick-it-Easy Robot powered by Covariant represents the industry benchmark for automated single-item picking in terms of suitability, reliability and performance and, in future, will be further developed by KNAPP and Covariant. The RFM-1 (Robotics Foundation Model) recently introduced by Covariant is an AI model designed to give robots human-like thinking capabilities and to take robotics solutions for intralogistics and more to the next level. The RFM-1 model was trained using text, image and video databases as well as numerical sensor readings collected during live operation. The technology is characterized by its ability to understand and autonomously handle a variety of scenarios. Instead of using rigid program codes to teach robots how to behave in complex situations, in future, robots will be able to learn like humans from the experience gained through countless observations. This multimodal approach allows the model to simulate the outcome of future scenarios and select the best course of action, improving both the speed and reliability of robot operations. RFM-1 sets a new level of flexibility and reliability in warehouse operations while opening the door to wider applications in the industry.