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A Robot for the Worst Job in the Warehouse

Boston Dynamics’ Stretch can move 800 heavy boxes per hour

4 min read
A robot near a conveyor belt with a box being placed by the robot.

Stretch can autonomously transfer boxes onto a roller conveyor fast enough to keep up with an experienced human worker.

Bob O’Connor
Yellow

As COVID-19 stresses global supply chains, the logistics industry is looking to automation to help keep workers safe and boost their efficiency. But there are many warehouse operations that don’t lend themselves to traditional automation—namely, tasks where the inputs and outputs of a process aren’t always well defined and can’t be completely controlled. A new generation of robots with the intelligence and flexibility to handle the kind of variation that people take in stride is entering warehouse environments. A prime example is Stretch, a new robot from Boston Dynamics that can move heavy boxes where they need to go just as fast as an experienced warehouse worker.

Stretch’s design is somewhat of a departure from the humanoid and quadrupedal robots that Boston Dynamics is best known for, such as Atlas and Spot. With its single massive arm, a gripper packed with sensors and an array of suction cups, and an omnidirectional mobile base, Stretch can transfer boxes that weigh as much as 50 pounds (23 kilograms) from the back of a truck to a conveyor belt at a rate of 800 boxes per hour. An experienced human worker can move boxes at a similar rate, but not all day long, whereas Stretch can go for 16 hours before recharging. And this kind of work is punishing on the human body, especially when heavy boxes have to be moved from near a trailer’s ceiling or floor.


“Truck unloading is one of the hardest jobs in a warehouse, and that's one of the reasons we're starting there with Stretch,” says Kevin Blankespoor, senior vice president of warehouse robotics at Boston Dynamics. Blankespoor explains that Stretch isn’t meant to replace people entirely; the idea is that multiple Stretch robots could make a human worker an order of magnitude more efficient. “Typically, you’ll have two people unloading each truck. Where we want to get with Stretch is to have one person unloading four or five trucks at the same time, using Stretches as tools.”

All Stretch needs is to be shown the back of a trailer packed with boxes, and it’ll autonomously go to work, placing each box on a conveyor belt one by one until the trailer is empty. People are still there to make sure that everything goes smoothly, and they can step in if Stretch runs into something that it can’t handle, but their full-time job becomes robot supervision instead of lifting heavy boxes all day.

“No one wants to do receiving.” —Matt Beane, UCSB

Achieving this level of reliable autonomy with Stretch has taken Boston Dynamics years of work, building on decades of experience developing robots that are strong, fast, and agile. Besides the challenge of building a high-performance robotic arm, the company also had to solve some problems that people find trivial but are difficult for robots, like looking at a wall of closely packed brown boxes and being able to tell where one stops and another begins.

Safety is also a focus, says Blankespoor, explaining that Stretch follows the standards for mobile industrial robots set by the American National Standards Institute and the Robotics Industry Association. That the robot operates inside a truck or trailer also helps to keep Stretch safely isolated from people working nearby, and at least for now, the trailer opening is fenced off while the robot is inside.

Stretch is optimized for moving boxes, a task that’s required throughout a warehouse. Boston Dynamics hopes that over the longer term the robot will be flexible enough to put its box-moving expertise to use wherever it’s needed. In addition to unloading trucks, Stretch has the potential to unload boxes from pallets, put boxes on shelves, build orders out of multiple boxes from different places in a warehouse, and ultimately load boxes onto trucks, a much more difficult problem than unloading due to the planning and precision required.

“Where we want to get with Stretch is to have one person unloading four or five trucks at the same time.” —Kevin Blankespoor, Boston Dynamics

In the short term, unloading a trailer (part of a warehouse job called “receiving”) is the best place for a robot like Stretch, agrees Matt Beane, who studies work involving robotics and AI at the University of California, Santa Barbara. “No one wants to do receiving,” he says. “It’s dangerous, tiring, and monotonous.”

But Beane, who for the last two years has led a team of field researchers in a nationwide study of automation in warehousing, points out that there may be important nuances to the job that a robot such as Stretch will probably miss, like interacting with the people who are working other parts of the receiving process. “There's subtle, high-bandwidth information being exchanged about boxes that humans down the line use as key inputs to do their job effectively, and I will be singularly impressed if Stretch can match that.”

Boston Dynamics spent much of 2021 turning Stretch from a prototype, built largely from pieces designed for Atlas and Spot, into a production-ready system that will begin shipping to a select group of customers in 2022, with broader sales expected in 2023. For Blankespoor, that milestone will represent just the beginning. He feels that such robots are poised to have an enormous impact on the logistics industry. “Despite the success of automation in manufacturing, warehouses are still almost entirely manually operated—we’re just starting to see a new generation of robots that can handle the variation you see in a warehouse, and that’s what we’re excited about with Stretch.”

The Conversation (1)
Daniel Malloy02 Jan, 2022
INDV

Bill Gates recommends taxing robots. AS much as I dont like Gates he has a good point

The World’s Largest Camera Is Nearly Complete

The future heart of the Vera C. Rubin Observatory will soon make its way to Chile

3 min read
A large black cylinder with a glass lens in front rests on a sturdy white structure in a bright room.

The LSST camera, eventually bound for the Vera C. Rubin Observatory in Chile, sits on its stand in a Bay Area clean room.

Jacqueline Ramseyer Orrell/SLAC National Accelerator Laboratory

The world’s largest camera sits within a nondescript industrial building in the hills above San Francisco Bay.

If all goes well, this camera will one day fit into the heart of the future Vera C. Rubin Observatory in Chile. For the last seven years, engineers have been crafting the camera in a clean room at the SLAC National Accelerator Laboratory in Menlo Park, Calif. In May 2023, if all goes according to plan, the camera will finally fly to its destination, itself currently under construction in the desert highlands of northern Chile.

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New nonprofit Basis hopes to model human reasoning to inform science and public policy

5 min read
ai hand and human hand touching pointer fingers
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The field of artificial intelligence has embraced deep learning—in which algorithms find patterns in big data sets—after moving on from earlier systems that more explicitly modeled human reasoning. But deep learning has its flaws: AI models often show a lack of common sense, for example. A new nonprofit, Basis, hopes to build software tools that advance the earlier method of modeling human reasoning, and then apply that method toward pressing problems in scientific discovery and public policy.

To date, Basis has received a government grant and a donation of a few million dollars. Advisors include Rui Costa, a neuroscientist who heads the Allen Institute in Seattle, and Anthony Philippakis, the chief data officer of the Broad Institute in Cambridge, Mass. In July, over tacos at the International Conference on Machine Intelligence, I spoke with Zenna Tavares, a Basis cofounder, and Sam Witty, a Basis research scientist, about human intelligence, problems with academia, and trash collection. The following transcript has been edited for brevity and clarity.

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How a Dual Curing Adhesive Works

UV22DC80-1 is an abrasion-resistant adhesive system that meets NASA low outgassing specs

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Master Bond's UV22DC80-1 is a one component, nanosilica filled, dual cure system with UV and heat curing mechanisms.

Master Bond

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