MIT alumnus-founded RightHand Robotics has developed picking robots that are more reliable and adaptable in warehouse environments.
For most people, the task of identifying an object, picking it up, and placing it somewhere else is trivial. For robots, it requires the latest in machine intelligence and robotic manipulation.
That’s what MIT spinoff RightHand Robotics has incorporated into its robotic piece-picking systems, which combine unique gripper designs with artificial intelligence and machine vision to help companies sort products and get orders out the door.
“If you buy something at the store, you push the cart down the aisle and pick it yourself. When you order online, there is an equivalent operation inside a fulfillment center,” says RightHand Robotics co-founder Lael Odhner ’04, SM ’06, PhD ’09. “The retailer typically needs to pick up single items, run them through a scanner, and put them into a sorter or conveyor belt to complete the order. It sounds easy until you imagine tens of thousands of orders a day and more than 100,000 unique products stored in a facility the size of 10 or 20 football fields, with the delivery expectation clock ticking.”
RightHand Robotics is helping companies respond to two broad trends that have transformed retail operations. One is the explosion of e-commerce, which only accelerated during the Covid-19 pandemic. The other is a shift to just-in-time inventory fulfillment, in which pharmacies, grocery stores, and apparel companies restock items based on what’s been purchased that day or week to improve efficiency.
