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#435824 A Q&A with Cruise’s head of AI, ...

Posted on September 26, 2019 by Android

In 2016, Cruise, an autonomous vehicle startup acquired by General Motors, had about 50 employees. At the beginning of 2019, the headcount at its San Francisco headquarters—mostly software engineers, mostly working on projects connected to machine learning and artificial intelligence—hit around 1000. Now that number is up to 1500, and by the end of this year it’s expected to reach about 2000, sprawling into a recently purchased building that had housed Dropbox. And that’s not counting the 200 or so tech workers that Cruise is aiming to install in a Seattle, Wash., satellite development center and a handful of others in Phoenix, Ariz., and Pasadena, Calif.

Cruise’s recent hires aren’t all engineers—it takes more than engineering talent to manage operations. And there are hundreds of so-called safety drivers that are required to sit in the 180 or so autonomous test vehicles whenever they roam the San Francisco streets. But that’s still a lot of AI experts to be hiring in a time of AI engineer shortages.

Hussein Mehanna, head of AI/ML at Cruise, says the company’s hiring efforts are on track, due to the appeal of the challenge of autonomous vehicles in drawing in AI experts from other fields. Mehanna himself joined Cruise in May from Google, where he was director of engineering at Google Cloud AI. Mehanna had been there about a year and a half, a relatively quick career stop after a short stint at Snap following four years working in machine learning at Facebook.

Mehanna has been immersed in AI and machine learning research since his graduate studies in speech recognition and natural language processing at the University of Cambridge. I sat down with Mehanna to talk about his career, the challenges of recruiting AI experts and autonomous vehicle development in general—and some of the challenges specific to San Francisco. We were joined by Michael Thomas, Cruise’s manager of AI/ML recruiting, who had also spent time recruiting AI engineers at Google and then Facebook.

IEEE Spectrum: When you were at Cambridge, did you think AI was going to take off like a rocket?

Mehanna: Did I imagine that AI was going to be as dominant and prevailing and sometimes hyped as it is now? No. I do recall in 2003 that my supervisor and I were wondering if neural networks could help at all in speech recognition. I remember my supervisor saying if anyone could figure out how use a neural net for speech he would give them a grant immediately. So he was on the right path. Now neural networks have dominated vision, speech, and language [processing]. But that boom started in 2012.

“In the early days, Facebook wasn’t that open to PhDs, it actually had a negative sentiment about researchers, and then Facebook shifted”

I didn’t [expect it], but I certainly aimed for it when [I was at] Microsoft, where I deliberately pushed my career towards machine learning instead of big data, which was more popular at the time. And [I aimed for it] when I joined Facebook.

In the early days, Facebook wasn’t that open to PhDs, or researchers. It actually had a negative sentiment about researchers. And then Facebook shifted to becoming one of the key places where PhD students wanted to do internships or join after they graduated. It was a mindset shift, they were [once] at a point in time where they thought what was needed for success wasn’t research, but now it’s different.

There was definitely an element of risk [in taking a machine learning career path], but I was very lucky, things developed very fast.

IEEE Spectrum: Is it getting harder or easier to find AI engineers to hire, given the reported shortages?

Mehanna: There is a mismatch [between job openings and qualified engineers], though it is hard to quantify it with numbers. There is good news as well: I see a lot more students diving deep into machine learning and data in their [undergraduate] computer science studies, so it’s not as bleak as it seems. But there is massive demand in the market.

Here at Cruise, demand for AI talent is just growing and growing. It might be is saturating or slowing down at other kinds of companies, though, [which] are leveraging more traditional applications—ad prediction, recommendations—that have been out there in the market for a while. These are more mature, better understood problems.

I believe autonomous vehicle technologies is the most difficult AI problem out there. The magnitude of the challenge of these problems is 1000 times more than other problems. They aren’t as well understood yet, and they require far deeper technology. And also the quality at which they are expected to operate is off the roof.

The autonomous vehicle problem is the engineering challenge of our generation. There’s a lot of code to write, and if we think we are going to hire armies of people to write it line by line, it’s not going to work. Machine learning can accelerate the process of generating the code, but that doesn’t mean we aren’t going to have engineers; we actually need a lot more engineers.

Sometimes people worry that AI is taking jobs. It is taking some developer jobs, but it is actually generating other developer jobs as well, protecting developers from the mundane and helping them build software faster and faster.

IEEE Spectrum: Are you concerned that the demand for AI in industry is drawing out the people in academia who are needed to educate future engineers, that is, the “eating the seed corn” problem?

Mehanna: There are some negative examples in the industry, but that’s not our style. We are looking for collaborations with professors, we want to cultivate a very deep and respectful relationship with universities.

And there’s another angle to this: Universities require a thriving industry for them to thrive. It is going to be extremely beneficial for academia to have this flourishing industry in AI, because it attracts more students to academia. I think we are doing them a fantastic favor by building these career opportunities. This is not the same as in my early days, [when] people told me “don’t go to AI; go to networking, work in the mobile industry; mobile is flourishing.”

IEEE Spectrum: Where are you looking as you try to find a thousand or so engineers to hire this year?

Thomas: We look for people who want to use machine learning to solve problems. They can be in many different industries—in the financial markets, in social media, in advertising. The autonomous vehicle industry is in its infancy. You can compare it to mobile in the early days: When the iPhone first came out, everyone was looking for developers with mobile experience, but you weren’t going to find them unless you went to straight to Apple, [so you had to hire other kinds of engineers]. This is the same type of thing: it is so new that you aren’t going to find experts in this area, because we are all still learning.

“You don’t have to be an autonomous vehicle expert to flourish in this world. It’s not too late to move…now would be a great time for AI experts working on other problems to shift their attention to autonomous vehicles.”

Mehanna: Because autonomous vehicle technology is the new frontier for AI experts, [the number of] people with both AI and autonomous vehicle experience is quite limited. So we are acquiring AI experts wherever they are, and helping them grow into the autonomous vehicle area. You don’t have to be an autonomous vehicle expert to flourish in this world. It’s not too late to move; even though there is a lot of great tech developed, there’s even more innovation ahead, so now would be a great time for AI experts working on other problems or applications to shift their attention to autonomous vehicles.

It feels like the Internet in 1980. It’s about to happen, but there are endless applications [to be developed over] the next few decades. Even if we can get a car to drive safely, there is the question of how can we tune the ride comfort, and then applying it all to different cities, different vehicles, different driving situations, and who knows to what other applications.

I can see how I can spend a lifetime career trying to solve this problem.

IEEE Spectrum: Why are you doing most of your development in San Francisco?

Mehanna: I think the best talent of the world is in Silicon Valley, and solving the autonomous vehicle problem is going to require the best of the best. It’s not just the engineering talent that is here, but [also] the entrepreneurial spirit. Solving the problem just as a technology is not going to be successful, you need to solve the product and the technology together. And the entrepreneurial spirit is one of the key reasons Cruise secured 7.5 billion in funding [besides GM, the company has a number of outside investors, including Honda, Softbank, and T. Rowe Price]. That [funding] is another reason Cruise is ahead of many others, because this problem requires deep resources.

“If you can do an autonomous vehicle in San Francisco you can do it almost anywhere.”

[And then there is the driving environment.] When I speak to my peers in the industry, they have a lot of respect for us, because the problems to solve in San Francisco technically are an order of magnitude harder. It is a tight environment, with a lot of pedestrians, and driving patterns that, let’s put it this way, are not necessarily the best in the nation. Which means we are seeing more problems ahead of our competitors, which gets us to better [software]. I think if you can do an autonomous vehicle in San Francisco you can do it almost anywhere.

A version of this post appears in the September 2019 print magazine as “AI Engineers: The Autonomous-Vehicle Industry Wants You.” Continue reading →

Posted in Human Robots

#435818 Swappable Flying Batteries Keep Drones ...

Posted on September 26, 2019 by Android

Battery power is a limiting factor for robots everywhere, but it’s particularly problematic for drones, which have to make an awkward tradeoff between the amount of battery they carry, the amount of other more useful stuff they carry, and how long they can spend in the air. Consumer drones seem to have settled around about a third of their overall mass in battery, resulting in flight times of 20 to 25 minutes at best, before you have to bring the drone back for a battery swap. And if whatever the drone was supposed to be doing depended on it staying in the air, then you’re pretty much out of luck.

When much larger aircraft have this problem, and in particular military aircraft which sometimes need to stay on-station for long periods of time, the solution is mid-air refueling—why send an aircraft all the way back to its fuel source when you can instead bring the fuel source to the aircraft? It’s easier to do this with liquid fuel than it is with batteries, of course, but researchers at UC Berkeley have come up with a clever solution: You just give the batteries wings. Or, in this case, rotors.

The big quadrotor, which weighs 820 grams, is carrying its own 2.2 Ah lithium-polymer battery that by itself gives it a flight time of about 12 minutes. Each little quadrotor weighs 320 g, including its own 0.8 Ah battery plus a 1.5 Ah battery as cargo. The little ones can’t keep themselves aloft for all that long, but that’s okay, because as flying batteries their only job is to go from ground to the big quadrotor and back again.

Photo: UC Berkeley

The flying batteries land on a tray mounted atop the main drone and align their legs with electrical contacts.

How the flying batteries work
As each flying battery approaches the main quadrotor, the smaller quadrotor takes a position about 30 centimeter above a passive docking tray mounted on top of the bigger drone. It then slowly descends to about 3 cm above, waits for its alignment to be just right, and then drops, landing on the tray which helps align its legs with electrical contacts. As soon as a connection is made, the main quadrotor is able to power itself completely from the smaller drone’s battery payload. Each flying battery can power the main quadrotor for about 6 minutes, and then it flies off and a new flying battery takes its place. If everything goes well, the main quadrotor only uses its primary battery during the undocking and docking phases, and in testing, this boosted its flight time from 12 minutes to nearly an hour.

All of this happens in a motion-capture environment, which is a big constraint, and getting this precision(ish) docking maneuver to work outside, or when the primary drone is moving, is something that the researchers would like to figure out. There are potential applications in situations where continuous monitoring by a drone is important—you could argue that switching off two identical drones might be a simpler way of achieving that, but it also requires two (presumably fancy) drones as opposed to just one plus a bunch of relatively simple and inexpensive flying batteries.

“Flying Batteries: In-flight Battery Switching to Increase Multirotor Flight Time,” by Karan P. Jain and Mark W. Mueller from the High Performance Robotics Lab at UC Berkeley, is available on arXiv. Continue reading →

Posted in Human Robots

#435806 Boston Dynamics’ Spot Robot Dog ...

Posted on September 24, 2019 by Android

Boston Dynamics is announcing this morning that Spot, its versatile quadruped robot, is now for sale. The machine’s animal-like behavior regularly electrifies crowds at tech conferences, and like other Boston Dynamics’ robots, Spot is a YouTube sensation whose videos amass millions of views.

Now anyone interested in buying a Spot—or a pack of them—can go to the company’s website and submit an order form. But don’t pull out your credit card just yet. Spot may cost as much as a luxury car, and it is not really available to consumers. The initial sale, described as an “early adopter program,” is targeting businesses. Boston Dynamics wants to find customers in select industries and help them deploy Spots in real-world scenarios.

“What we’re doing is the productization of Spot,” Boston Dynamics CEO Marc Raibert tells IEEE Spectrum. “It’s really a milestone for us going from robots that work in the lab to these that are hardened for work out in the field.”

Boston Dynamics has always been a secretive company, but last month, in preparation for launching Spot (formerly SpotMini), it allowed our photographers into its headquarters in Waltham, Mass., for a special shoot. In that session, we captured Spot and also Atlas—the company’s highly dynamic humanoid—in action, walking, climbing, and jumping.

You can see Spot’s photo interactives on our Robots Guide. (The Atlas interactives will appear in coming weeks.)

Gif: Bob O’Connor/Robots.ieee.org

And if you’re in the market for a robot dog, here’s everything we know about Boston Dynamics’ plans for Spot.

Who can buy a Spot?
If you’re interested in one, you should go to Boston Dynamics’ website and take a look at the information the company requires from potential buyers. Again, the focus is on businesses. Boston Dynamics says it wants to get Spots out to initial customers that “either have a compelling use case or a development team that we believe can do something really interesting with the robot,” says VP of business development Michael Perry. “Just because of the scarcity of the robots that we have, we’re going to have to be selective about which partners we start working together with.”

What can Spot do?
As you’ve probably seen on the YouTube videos, Spot can walk, trot, avoid obstacles, climb stairs, and much more. The robot’s hardware is almost completely custom, with powerful compute boards for control, and five sensor modules located on every side of Spot’s body, allowing it to survey the space around itself from any direction. The legs are powered by 12 custom motors with a reduction, with a top speed of 1.6 meters per second. The robot can operate for 90 minutes on a charge. In addition to the basic configuration, you can integrate up to 14 kilograms of extra hardware to a payload interface. Among the payload packages Boston Dynamics plans to offer are a 6 degrees-of-freedom arm, a version of which can be seen in some of the YouTube videos, and a ring of cameras called SpotCam that could be used to create Street View–type images inside buildings.

Image: Boston Dynamics

How do you control Spot?
Learning to drive the robot using its gaming-style controller “takes 15 seconds,” says CEO Marc Raibert. He explains that while teleoperating Spot, you may not realize that the robot is doing a lot of the work. “You don’t really see what that is like until you’re operating the joystick and you go over a box and you don’t have to do anything,” he says. “You’re practically just thinking about what you want to do and the robot takes care of everything.” The control methods have evolved significantly since the company’s first quadruped robots, machines like BigDog and LS3. “The control in those days was much more monolithic, and now we have what we call a sequential composition controller,” Raibert says, “which lets the system have control of the dynamics in a much broader variety of situations.” That means that every time one of Spot’s feet touches or doesn’t touch the ground, this different state of the body affects the basic physical behavior of the robot, and the controller adjusts accordingly. “Our controller is designed to understand what that state is and have different controls depending upon the case,” he says.

How much does Spot cost?
Boston Dynamics would not give us specific details about pricing, saying only that potential customers should contact them for a quote and that there is going to be a leasing option. It’s understandable: As with any expensive and complex product, prices can vary on a case by case basis and depend on factors such as configuration, availability, level of support, and so forth. When we pressed the company for at least an approximate base price, Perry answered: “Our general guidance is that the total cost of the early adopter program lease will be less than the price of a car—but how nice a car will depend on the number of Spots leased and how long the customer will be leasing the robot.”

Can Spot do mapping and SLAM out of the box?
The robot’s perception system includes cameras and 3D sensors (there is no lidar), used to avoid obstacles and sense the terrain so it can climb stairs and walk over rubble. It’s also used to create 3D maps. According to Boston Dynamics, the first software release will offer just teleoperation. But a second release, to be available in the next few weeks, will enable more autonomous behaviors. For example, it will be able to do mapping and autonomous navigation—similar to what the company demonstrated in a video last year, showing how you can drive the robot through an environment, create a 3D point cloud of the environment, and then set waypoints within that map for Spot to go out and execute that mission. For customers that have their own autonomy stack and are interested in using those on Spot, Boston Dynamics made it “as plug and play as possible in terms of how third-party software integrates into Spot’s system,” Perry says. This is done mainly via an API.

How does Spot’s API works?
Boston Dynamics built an API so that customers can create application-level products with Spot without having to deal with low-level control processes. “Rather than going and building joint-level kinematic access to the robot,” Perry explains, “we created a high-level API and SDK that allows people who are used to Web app development or development of missions for drones to use that same scope, and they’ll be able to build applications for Spot.”

What applications should we see first?
Boston Dynamics envisions Spot as a platform: a versatile mobile robot that companies can use to build applications based on their needs. What types of applications? The company says the best way to find out is to put Spot in the hands of as many users as possible and let them develop the applications. Some possibilities include performing remote data collection and light manipulation in construction sites; monitoring sensors and infrastructure at oil and gas sites; and carrying out dangerous missions such as bomb disposal and hazmat inspections. There are also other promising areas such as security, package delivery, and even entertainment. “We have some initial guesses about which markets could benefit most from this technology, and we’ve been engaging with customers doing proof-of-concept trials,” Perry says. “But at the end of the day, that value story is really going to be determined by people going out and exploring and pushing the limits of the robot.”

Photo: Bob O'Connor

How many Spots have been produced?
Last June, Boston Dynamics said it was planning to build about a hundred Spots by the end of the year, eventually ramping up production to a thousand units per year by the middle of this year. The company admits that it is not quite there yet. It has built close to a hundred beta units, which it has used to test and refine the final design. This version is now being mass manufactured, but the company is still “in the early tens of robots,” Perry says.

How did Boston Dynamics test Spot?

The company has tested the robots during proof-of-concept trials with customers, and at least one is already using Spot to survey construction sites. The company has also done reliability tests at its facility in Waltham, Mass. “We drive around, not quite day and night, but hundreds of miles a week, so that we can collect reliability data and find bugs,” Raibert says.

What about competitors?
In recent years, there’s been a proliferation of quadruped robots that will compete in the same space as Spot. The most prominent of these is ANYmal, from ANYbotics, a Swiss company that spun out of ETH Zurich. Other quadrupeds include Vision from Ghost Robotics, used by one of the teams in the DARPA Subterranean Challenge; and Laikago and Aliengo from Unitree Robotics, a Chinese startup. Raibert views the competition as a positive thing. “We’re excited to see all these companies out there helping validate the space,” he says. “I think we’re more in competition with finding the right need [that robots can satisfy] than we are with the other people building the robots at this point.”

Why is Boston Dynamics selling Spot now?
Boston Dynamics has long been an R&D-centric firm, with most of its early funding coming from military programs, but it says commercializing robots has always been a goal. Productizing its machines probably accelerated when the company was acquired by Google’s parent company, Alphabet, which had an ambitious (and now apparently very dead) robotics program. The commercial focus likely continued after Alphabet sold Boston Dynamics to SoftBank, whose famed CEO, Masayoshi Son, is known for his love of robots—and profits.

Which should I buy, Spot or Aibo?
Don’t laugh. We’ve gotten emails from individuals interested in purchasing a Spot for personal use after seeing our stories on the robot. Alas, Spot is not a bigger, fancier Aibo pet robot. It’s an expensive, industrial-grade machine that requires development and maintenance. If you’re maybe Jeff Bezos you could probably convince Boston Dynamics to sell you one, but otherwise the company will prioritize businesses.

What’s next for Boston Dynamics?
On the commercial side of things, other than Spot, Boston Dynamics is interested in the logistics space. Earlier this year it announced the acquisition of Kinema Systems, a startup that had developed vision sensors and deep-learning software to enable industrial robot arms to locate and move boxes. There’s also Handle, the mobile robot on whegs (wheels + legs), that can pick up and move packages. Boston Dynamics is hiring both in Waltham, Mass., and Mountain View, Calif., where Kinema was located.

Okay, can I watch a cool video now?
During our visit to Boston Dynamics’ headquarters last month, we saw Atlas and Spot performing some cool new tricks that we unfortunately are not allowed to tell you about. We hope that, although the company is putting a lot of energy and resources into its commercial programs, Boston Dynamics will still find plenty of time to improve its robots, build new ones, and of course, keep making videos. [Update: The company has just released a new Spot video, which we’ve embedded at the top of the post.][Update 2: We should have known. Boston Dynamics sure knows how to create buzz for itself: It has just released a second video, this time of Atlas doing some of those tricks we saw during our visit and couldn’t tell you about. Enjoy!]

[ Boston Dynamics ] Continue reading →

Posted in Human Robots

#435804 New AI Systems Are Here to Personalize ...

Posted on September 24, 2019 by Android

The narratives about automation and its impact on jobs go from urgent to hopeful and everything in between. Regardless where you land, it’s hard to argue against the idea that technologies like AI and robotics will change our economy and the nature of work in the coming years.

A recent World Economic Forum report noted that some estimates show automation could displace 75 million jobs by 2022, while at the same time creating 133 million new roles. While these estimates predict a net positive for the number of new jobs in the coming decade, displaced workers will need to learn new skills to adapt to the changes. If employees can’t be retrained quickly for jobs in the changing economy, society is likely to face some degree of turmoil.

According to Bryan Talebi, CEO and founder of AI education startup Ahura AI, the same technologies erasing and creating jobs can help workers bridge the gap between the two.

Ahura is developing a product to capture biometric data from adult learners who are using computers to complete online education programs. The goal is to feed this data to an AI system that can modify and adapt their program to optimize for the most effective teaching method.

While the prospect of a computer recording and scrutinizing a learner’s behavioral data will surely generate unease across a society growing more aware and uncomfortable with digital surveillance, some people may look past such discomfort if they experience improved learning outcomes. Users of the system would, in theory, have their own personalized instruction shaped specifically for their unique learning style.

And according to Talebi, their systems are showing some promise.

“Based on our early tests, our technology allows people to learn three to five times faster than traditional education,” Talebi told me.

Currently, Ahura’s system uses the video camera and microphone that come standard on the laptops, tablets, and mobile devices most students are using for their learning programs.

With the computer’s camera Ahura can capture facial movements and micro expressions, measure eye movements, and track fidget score (a measure of how much a student moves while learning). The microphone tracks voice sentiment, and the AI leverages natural language processing to review the learner’s word usage.

From this collection of data Ahura can, according to Talebi, identify the optimal way to deliver content to each individual.

For some users that might mean a video tutorial is the best style of learning, while others may benefit more from some form of experiential or text-based delivery.

“The goal is to alter the format of the content in real time to optimize for attention and retention of the information,” said Talebi. One of Ahura’s main goals is to reduce the frequency with which students switch from their learning program to distractions like social media.

“We can now predict with a 60 percent confidence interval ten seconds before someone switches over to Facebook or Instagram. There’s a lot of work to do to get that up to a 95 percent level, so I don’t want to overstate things, but that’s a promising indication that we can work to cut down on the amount of context-switching by our students,” Talebi said.

Talebi repeatedly mentioned his ambition to leverage the same design principles used by Facebook, Twitter, and others to increase the time users spend on those platforms, but instead use them to design more compelling and even addictive education programs that can compete for attention with social media.

But the notion that Ahura’s system could one day be used to create compelling or addictive education necessarily presses against a set of justified fears surrounding data privacy. Growing anxiety surrounding the potential to misuse user data for social manipulation is widespread.

“Of course there is a real danger, especially because we are collecting so much data about our users which is specifically connected to how they consume content. And because we are looking so closely at the ways people interact with content, it’s incredibly important that this technology never be used for propaganda or to sell things to people,” Talebi tried to assure me.

Unsurprisingly (and worrying), using this AI system to sell products to people is exactly where some investors’ ambitions immediately turn once they learn about the company’s capabilities, according to Talebi. During our discussion Talebi regularly cited the now infamous example of Cambridge Analytica, the political consulting firm hired by the Trump campaign to run a psychographically targeted persuasion campaign on the US population during the most recent presidential election.

“It’s important that we don’t use this technology in those ways. We’re aware that things can go sideways, so we’re hoping to put up guardrails to ensure our system is helping and not harming society,” Talebi said.

Talebi will surely need to take real action on such a claim, but says the company is in the process of identifying a structure for an ethics review board—one that carries significant influence with similar voting authority as the executive team and the regular board.

“Our goal is to build an ethics review board that has teeth, is diverse in both gender and background but also in thought and belief structures. The idea is to have our ethics review panel ensure we’re building things ethically,” he said.

Data privacy appears to be an important issue for Talebi, who occasionally referenced a major competitor in the space based in China. According to a recent article from MIT Tech Review outlining the astonishing growth of AI-powered education platforms in China, data privacy concerns may be less severe there than in the West.

Ahura is currently developing upgrades to an early alpha-stage prototype, but is already capturing data from students from at least one Ivy League school and a variety of other places. Their next step is to roll out a working beta version to over 200,000 users as part of a partnership with an unnamed corporate client who will be measuring the platform’s efficacy against a control group.

Going forward, Ahura hopes to add to its suite of biometric data capture by including things like pupil dilation and facial flushing, heart rate, sleep patterns, or whatever else may give their system an edge in improving learning outcomes.

As information technologies increasingly automate work, it’s likely we’ll also see rapid changes to our labor systems. It’s also looking increasingly likely that those same technologies will be used to improve our ability to give people the right skills when they need them. It may be one way to address the challenges automation is sure to bring.

Image Credit: Gerd Altmann / Pixabay Continue reading →

Posted in Human Robots

#435784 Amazon Uses 800 Robots to Run This ...

Posted on September 20, 2019 by Android

At Amazon’s re:MARS conference in Las Vegas today, who else but Amazon is introducing two new robots designed to make its fulfillment centers even more fulfilling. Xanthus (named after a mythological horse that could very briefly talk but let’s not read too much into that) is a completely redesigned drive unit, one of the robotic mobile bases that carries piles of stuff around for humans to pick from. It has a thinner profile, a third of the parts, costs half as much, and can wear different modules on top to perform a much wider variety of tasks than its predecessor.

Pegasus (named after a mythological horse that could fly but let’s not read too much into that either) is also a mobile robot, but much smaller than Xanthus, designed to help the company quickly and accurately sort individual packages. For Amazon, it’s a completely new large-scale robotic system involving tightly coordinated fleets of robots tossing boxes down chutes, and it’s just as fun to watch as it sounds.

Amazon has 800 Pegasus units already deployed at a sorting facility in the United States, adding to their newly updated total of 200,000 robotic drive units worldwide.

If the Pegasus system looks familiar, it’s because other warehouse automation companies have had something that’s at least superficially very similar up and running for years.

Photo: Amazon

Pegasus is one of Amazon’s new warehouse robots, equipped with a conveyor belt on top and used in the company’s sorting facilities.

But the most interesting announcement that Amazon made, kind of low key and right at the end of their re:MARS talk, is that they’re working on ways of making some of their mobile robots actually collaborative, leveraging some of the technology that they acquired from Boulder, Colo.-based warehouse robotics startup Canvas Technology earlier this year:

“With our recent acquisition of Canvas, we expect to be able to combine this drive platform with AI and autonomous mobility capabilities, and for the first time, allow our robots to move outside of our robotic drive fields, and interact collaboratively with our associates to do a number of mobility tasks,” said Brad Porter, VP of robotics at Amazon.

At the moment, Amazon’s robots are physically separated from humans except for one highly structured station where the human only interacts with the robot in one or two very specific ways. We were told a few months ago that Amazon would like to have mobile robots that are able to move things through the areas of fulfillment centers that have people in them, but that they’re (quite rightly) worried about the safety aspects of having robots and humans work around each other. Other companies are already doing this on a smaller scale, and it means developing a reliable safety system that can handle randomly moving humans, environmental changes, and all kinds of other stuff. It’s much more difficult than having a nice, clean, roped-off area to work in where a wayward human would be an exception rather than just another part of the job.

Photo: Canvas Technology

A robot created by Canvas Technology, a Boulder, Colo.-based warehouse robotics startup acquired by Amazon earlier this year.

It now seems like Canvas has provided the secret sauce that Amazon needed to start implementing this level of autonomy. As for what it’s going to look like, our best guess is that Amazon is going to have to do a little bit more than slap some extra sensors onto Xanthus or Pegasus, if for no other reason than the robots will almost certainly need more ground clearance to let them operate away from the reliably flat floors that they’re accustomed to. We’re expecting to see them performing many of the tasks that companies like Fetch Robotics and OTTO Motors are doing already—moving everything from small boxes to large pallets to keep humans from having to waste time walking.

Of course, this all feeds back into what drives Amazon more than anything else: efficiency. And for better or worse, humans are not uniquely good at moving things from place to place, so it’s no surprise that Amazon wants to automate that, too. The good news is that, at least for now, Amazon still needs humans to babysit all those robots.

[ Amazon ] Continue reading →

Posted in Human Robots