As far as I can make out, Amazon’s warehouses are highly structured, extremely organized, very tidy, absolute raging messes. Everything in an Amazon warehouse is (usually) exactly where it’s supposed to be, which is typically jammed into some pseudorandom fabric bin the size of a shoebox along with a bunch of other pseudorandom crap. Somehow, this turns out to be the most space- and time-efficient way of doing things, because (as we’ve written about before) you have to consider the process of stowing items away in a warehouse as well as the process of picking them, and that involves some compromises in favor of space and speed.
For humans, this isn’t so much of a problem. When someone orders something on Amazon, a human can root around in those bins, shove some things out of the way, and then pull out the item that they’re looking for. This is exactly the sort of thing that robots tend to be terrible at, because not only is this process slightly different every single time, it’s also very hard to define exactly how humans go about it.
As you might expect, Amazon has been working very very hard on this picking problem. Today at an event in Germany, the company announced Vulcan, a robotic system that can both stow and pick items at human(ish) speeds.
Last time we talked with Aaron Parness, the director of applied science at Amazon Robotics, our conversation was focused on stowing—putting items into bins. As part of today’s announcement, Amazon revealed that its robots are now slightly faster at stowing than the average human is. But in the stow context, there’s a limited amount that a robot really has to understand about what’s actually happening in the bin. Fundamentally, the stowing robot’s job is to squoosh whatever is currently in a bin as far to one side as possible in order to make enough room to cram a new item in. As long as the robot is at least somewhat careful not to crushify anything, it’s a relatively straightforward task, at least compared to picking.
The choices made when an item is stowed into a bin will affect how hard it is to get that item out of that bin later on—this is called “bin etiquette.” Amazon is trying to learn bin etiquette with AI to make picking more efficient.Amazon
The defining problem of picking, as far as robots are concerned, is sensing and manipulation in clutter. “It’s a naturally contact-rich task, and we have to plan on that contact and react to it,” Parness says. And it’s not enough to solve these problems slowly and carefully, because Amazon Robotics is trying to put robots in production, which means that its systems are being directly compared to a not-so-small army of humans who are doing this exact same job very efficiently.
“There’s a new science challenge here, which is to identify the right item,” explains Parness. The thing to understand about identifying items in an Amazon warehouse is that there are a lot of them: something like 400 million unique items. One single floor of an Amazon warehouse can easily contain 15,000 pods, which is over a million bins, and Amazon has several hundred warehouses. This is a lot of stuff.
In theory, Amazon knows exactly which items are in every single bin. Amazon also knows (again, in theory), the weight and dimensions of each of those items, and probably has some pictures of each item from previous times that the item has been stowed or picked. This is a great starting point for item identification, but as Parness points out, “We have lots of items that aren’t feature rich—imagine all of the different things you might get in a brown cardboard box.”
Clutter and Contact
As challenging as it is to correctly identify an item in a bin that may be stuffed to the brim with nearly identical items, an even bigger challenge is actually getting that item that you just identified out of the bin. The hardware and software that humans have for doing this task is unmatched by any robot, which is always a problem, but the real complicating factor is dealing with items that are all jumbled together in a small fabric bin. And the picking process itself involves more than just extraction—once the item is out of the bin, you then have to get it to the next order-fulfillment step, which means dropping it into another bin or putting it on a conveyor or something.
“When we were originally starting out, we assumed we’d have to carry the item over some distance after we pulled it out of the bin,” explains Parness. “So we were thinking we needed pinch grasping.” A pinch grasp is when you grab something between a finger (or fingers) and your thumb, and at least for humans, it’s a versatile and reliable way of grabbing a wide variety of stuff. But as Parness notes, for robots in this context, it’s more complicated: “Even pinch grasping is not ideal because if you pinch the edge of a book, or the end of a plastic bag with something inside it, you don’t have pose control of the item and it may flop around unpredictably.”
At some point, Parness and his team realized that while an item did have to move farther than just out of the bin, it didn’t actually have to get moved by the picking robot itself. Instead, they came up with a lifting conveyor that positions itself directly outside of the bin being picked from, so that all the robot has to do is get the item out of the bin and onto the conveyor. “It doesn’t look that graceful right now,” admits Parness, but it’s a clever use of hardware to substantially simplify the manipulation problem, and has the side benefit of allowing the robot to work more efficiently, since the conveyor can move the item along while the arm starts working on the next pick.
Amazon’s robots have different techniques for extracting items from bins, using different gripping hardware depending on what needs to be picked. The type of end effector that the system chooses and the grasping approach depend on what the item is, where it is in the bin, and also what it’s next to. It’s a complicated planning problem that Amazon is tackling with AI, as Parness explains. “We’re starting to build foundation models of items, including properties like how squishy they are, how fragile they are, and whether they tend to get stuck on other items or no. So we’re trying to learn those things, and it’s early stage for us, but we think reasoning about item properties is going to be important to get to that level of reliability that we need.”
Reliability has to be superhigh for Amazon (and with many other commercial robotic deployments) simply because small errors multiplied over huge deployments result in an unacceptable amount of screwing up. There’s a very, very long tail of unusual things that Amazon’s robots might encounter when trying to extract an item from a bin. Even if there’s some particularly weird bin situation that might only show up once in a million picks, that still ends up happening many times per day on the scale at which Amazon operates. Fortunately for Amazon, they’ve got humans around, and part of the reason that this robotic system can be effective in production at all is that if the robot gets stuck, or even just sees a bin that it knows is likely to cause problems, it can just give up, route that particular item to a human picker, and move on to the next one.
The other new technique that Amazon is implementing is a sort of modern approach to “visual servoing,” where the robot watches itself move and then adjusts its movement based on what it sees. As Parness explains: “It’s an important capability because it allows us to catch problems before they happen. I think that’s probably our biggest innovation, and it spans not just our problem, but problems across robotics.”
A (More) Automated Future
Parness was very clear that (for better or worse) Amazon isn’t thinking about its stowing and picking robots in terms of replacing humans completely. There’s that long tail of items that need a human touch, and it’s frankly hard to imagine any robotic-manipulation system capable enough to make at least occasional human help unnecessary in an environment like an Amazon warehouse, which somehow manages to maximize organization and chaos at the same time.
These stowing and picking robots have been undergoing live testing in an Amazon warehouse in Germany for the past year, where they’re already demonstrating ways in which human workers could directly benefit from their presence. For example, Amazon pods can be up to 2.5 meters tall, meaning that human workers need to use a stepladder to reach the highest bins and bend down to reach the lowest ones. If the robots were primarily tasked with interacting with these bins, it would help humans work faster while putting less stress on their bodies.
With the robots so far managing to keep up with human workers, Parness tells us that the emphasis going forward will be primarily on getting better at not screwing up: “I think our speed is in a really good spot. The thing we’re focused on now is getting that last bit of reliability, and that will be our next year of work.” While it may seem like Amazon is optimizing for its own very specific use cases, Parness reiterates that the bigger picture here is using every last one of those 400 million items jumbled into bins as a unique opportunity to do fundamental research on fast, reliable manipulation in complex environments.
“If you can build the science to handle high contact and high clutter, we’re going to use it everywhere,” says Parness. “It’s going to be useful for everything, from warehouses to your own home. What we’re working on now are just the first problems that are forcing us to develop these capabilities, but I think it’s the future of robotic manipulation.”
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