Rising consumer demand for products through e-commerce has created incredible pressure on companies to improve their fulfillment operations, while at the same time they deal with a growing labor shortage for warehouse workers. Many have turned to robot companies to help them with this problem, including ones that are using robots to help pick and sort items through the use of artificial intelligence.
RBR50 2019 honoree Kindred.AI has developed systems that enable robots to interact with the physical world for the past five years. Its SORT system for piece-picking in the order fulfillment space includes the company’s AutoGrasp Robotics Intelligence Platform, which uses advanced AI algorithms in vision, grasping, and manipulation.
Robotics Business Review recently spoke with Victor Anjos, the company’s vice president of engineering, about recent developments driving the AI piece-picking space, how the company plans to make its robots even smarter, and how it reacts to manufacturers creating more and more products for robots to identify and manipulate.
Q: In the past few years, we’ve really seen piece-picking applications and companies emerge pretty dramatically. Is there a particular part of the technology that has enabled this growth – whether it’s computer vision, AI/machine learning, grasping/tools – or was it a “perfect storm” of factors all combining to create these achievements?
Anjos: It’s due to a multitude of reasons. One is the downward pressure in the market coming from the big players in automation. To meet the consumer’s expectation for fast, free shipping, fulfillment centers to have to accelerate the process, without sacrificing completeness or accuracy. But hourly workforces are hard to find, staff and retain. The work is tedious and can lead to injury, so robots make more sense in those situations.
On the technology side, the advent of better computer vision systems, AI models, and compute capacity (through either GPUs or cloud infrastructure), combined with recent advances in machine learning have been paramount in driving the success of piece-picking systems, which is the area where Kindred shines the best.
Q: As you’ve developed the system for grabbing and sorting items in an e-commerce situation, what types of products have proven most difficult to master for the system? Is continual work needed to be able to grab those items, or are we at a point where a company will just use workers to grab those really hard items?
Anjos: Our customers expect our robots to be able to pick a majority of their items, and we are constantly refining our ability to do so. Our SORT robots come in several different configurations that are optimized for a variety of items like polybags, shoeboxes, small general merchandise items, and so on.
It’s important to understand that the challenge is broader than just picking up items with a variety of form factors. It’s about identifying items in a dynamic, fast-paced fulfillment environment. Then the item has to be grasped using human-like dexterity and placed in a specific area. All of those steps must be done very quickly, consistently, with as little wasted motion as possible. In some cases, we rely on a remote pilot to coach the robot on how to handle oddly-shaped or irregularly-sized items. In that case, the exception becomes a learning opportunity for SORT, our robot.