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Every year, for just a few days in a major city, a small team of roboticists get to live the dream: ordering around their own personal robot butlers. In carefully-constructed replicas of a restaurant scene or a domestic setting, these robots perform any number of simple algorithmic tasks. “Get the can of beans from the shelf. Greet the visitors to the museum. Help the humans with their shopping. Serve the customers at the restaurant.”
This is Robocup @ Home, the annual tournament where teams of roboticists put their autonomous service robots to the test for practical domestic applications. The tasks seem simple and mundane, but considering the technology required reveals that they’re really not.
The Robot Butler Contest
Say you want a robot to fetch items in the supermarket. In a crowded, noisy environment, the robot must understand your commands, ask for clarification, and map out and navigate an unfamiliar environment, avoiding obstacles and people as it does so. Then it must recognize the product you requested, perhaps in a cluttered environment, perhaps in an unfamiliar orientation. It has to grasp that product appropriately—recall that there are entire multi-million-dollar competitions just dedicated to developing robots that can grasp a range of objects—and then return it to you.
It’s a job so simple that a child could do it—and so complex that teams of smart roboticists can spend weeks programming and engineering, and still end up struggling to complete simplified versions of this task. Of course, the child has the advantage of millions of years of evolutionary research and development, while the first robots that could even begin these tasks were only developed in the 1970s.
Even bearing this in mind, Robocup @ Home can feel like a place where futurist expectations come crashing into technologist reality. You dream of a smooth-voiced, sardonic JARVIS who’s already made your favorite dinner when you come home late from work; you end up shouting “remember the biscuits” at a baffled, ungainly droid in aisle five.
Caring for the Elderly
Famously, Japan is one of the most robo-enthusiastic nations in the world; they are the nation that stunned us all with ASIMO in 2000, and several studies have been conducted into the phenomenon. It’s no surprise, then, that humanoid robotics should be seriously considered as a solution to the crisis of the aging population. The Japanese government, as part of its robots strategy, has already invested $44 million in their development.
Toyota’s Human Support Robot (HSR-2) is a simple but programmable robot with a single arm; it can be remote-controlled to pick up objects and can monitor patients. HSR-2 has become the default robot for use in Robocup @ Home tournaments, at least in tasks that involve manipulating objects.
Alongside this, Toyota is working on exoskeletons to assist people in walking after strokes. It may surprise you to learn that nurses suffer back injuries more than any other occupation, at roughly three times the rate of construction workers, due to the day-to-day work of lifting patients. Toyota has a Care Assist robot/exoskeleton designed to fix precisely this problem by helping care workers with the heavy lifting.
The Home of the Future
The enthusiasm for domestic robotics is easy to understand and, in fact, many startups already sell robots marketed as domestic helpers in some form or another. In general, though, they skirt the immensely complicated task of building a fully capable humanoid robot—a task that even Google’s skunk-works department gave up on, at least until recently.
It’s plain to see why: far more research and development is needed before these domestic robots could be used reliably and at a reasonable price. Consumers with expectations inflated by years of science fiction saturation might find themselves frustrated as the robots fail to perform basic tasks.
Instead, domestic robotics efforts fall into one of two categories. There are robots specialized to perform a domestic task, like iRobot’s Roomba, which stuck to vacuuming and became the most successful domestic robot of all time by far.
The tasks need not necessarily be simple, either: the impressive but expensive automated kitchen uses the world’s most dexterous hands to cook meals, providing it can recognize the ingredients. Other robots focus on human-robot interaction, like Jibo: they essentially package the abilities of a voice assistant like Siri, Cortana, or Alexa to respond to simple questions and perform online tasks in a friendly, dynamic robot exterior.
In this way, the future of domestic automation starts to look a lot more like smart homes than a robot or domestic servant. General robotics is difficult in the same way that general artificial intelligence is difficult; competing with humans, the great all-rounders, is a challenge. Getting superhuman performance at a more specific task, however, is feasible and won’t cost the earth.
Individual startups without the financial might of a Google or an Amazon can develop specialized robots, like Seven Dreamers’ laundry robot, and hope that one day it will form part of a network of autonomous robots that each have a role to play in the household.
The Smart Home has been a staple of futurist expectations for a long time, to the extent that movies featuring smart homes out of control are already a cliché. But critics of the smart home idea—and of the internet of things more generally—tend to focus on the idea that, more often than not, software just adds an additional layer of things that can break (NSFW), in exchange for minimal added convenience. A toaster that can short-circuit is bad enough, but a toaster that can refuse to serve you toast because its firmware is updating is something else entirely.
That’s before you even get into the security vulnerabilities, which are all the more important when devices are installed in your home and capable of interacting with them. The idea of a smart watch that lets you keep an eye on your children might sound like something a security-conscious parent would like: a smart watch that can be hacked to track children, listen in on their surroundings, and even fool them into thinking a call is coming from their parents is the stuff of nightmares.
Key to many of these problems is the lack of standardization for security protocols, and even the products themselves. The idea of dozens of startups each developing a highly-specialized piece of robotics to perform a single domestic task sounds great in theory, until you realize the potential hazards and pitfalls of getting dozens of incompatible devices to work together on the same system.
It seems inevitable that there are yet more layers of domestic drudgery that can be automated away, decades after the first generation of time-saving domestic devices like the dishwasher and vacuum cleaner became mainstream. With projected market values into the billions and trillions of dollars, there is no shortage of industry interest in ironing out these kinks. But, for now at least, the answer to the question: “Where’s my robot butler?” is that it is gradually, painstakingly learning how to sort through groceries.
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Shopping is becoming less and less of a consumer experience—or, for many, less of a chore—as the list of things that can be bought online and delivered to our homes grows to include, well, almost anything you can think of. An Israeli startup is working to make shopping and deliveries even faster and cheaper—and they’re succeeding.
Last week, CommonSense Robotics announced the launch of its first autonomous micro-fulfillment center in Tel Aviv. The company claims the facility is the smallest of its type in the world at 6,000 square feet. For comparison’s sake—most fulfillment hubs that incorporate robotics are at least 120,000 square feet. Amazon’s upcoming facility in Bessemer, Alabama will be a massive 855,000 square feet.
The thing about a building whose square footage is in the hundred-thousands is, you can fit a lot of stuff inside it, but there aren’t many places you can fit the building itself, especially not in major urban areas. So most fulfillment centers are outside cities, which means more time and more money to get your Moroccan oil shampoo, or your vegetable garden starter kit, or your 100-pack of organic protein bars from that fulfillment center to your front door.
CommonSense Robotics built the Tel Aviv center in an area that was previously thought too small for warehouse infrastructure. “In order to fit our site into small, tight urban spaces, we’ve designed every single element of it to optimize for space efficiency,” said Avital Sterngold, VP of operations. Using a robotic sorting system that includes hundreds of robots, plus AI software that assigns them specific tasks, the facility can prepare orders in less than five minutes end-to-end.
It’s not all automated, though—there’s still some human labor in the mix. The robots fetch goods and bring them to a team of people, who then pack the individual orders.
CommonSense raised $20 million this year in a funding round led by Palo Alto-based Playground Global. The company hopes to expand its operations to the US and UK in 2019. Its business model is to charge retailers a fee for each order fulfilled, while maintaining ownership and operation of the fulfillment centers. The first retailers to jump on the bandwagon were Super-Pharm, a drugstore chain, and Rami Levy, a retail supermarket chain.
“Staying competitive in today’s market is anchored by delivering orders quickly and determining how to fulfill and deliver orders efficiently, which are always the most complex aspects of any ecommerce operation. With robotics, we will be able to fulfill and deliver orders in under one hour, all while saving costs on said fulfillment and delivery,” said Super-Pharm VP Yossi Cohen. “Before CommonSense Robotics, we offered our customers next-day home delivery. With this partnership, we are now able to offer our customers same-day delivery and will very soon be offering them one-hour delivery.”
Long live the instant gratification economy—and the increasingly sophisticated technology that’s enabling it.
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Each time there’s a headline about driverless trucking technology, another piece is taken out of the old equation. First, an Uber/Otto truck’s safety driver went hands-off once the truck reached the highway (and said truck successfully delivered its valuable cargo of 50,000 beers). Then, Starsky Robotics announced its trucks would start making autonomous deliveries without a human in the vehicle at all.
Now, Volvo has taken the tech one step further. Its new trucks not only won’t have safety drivers, they won’t even have the option of putting safety drivers behind the wheel, because there is no wheel—and no cab, either.
Vera, as the technology’s been dubbed, was unveiled in September, and consists of a sort of flat-Tesla-like electric car with a standard trailer hookup. The vehicles are connected to a cloud service, which also connects them to each other and to a control center. The control center monitors the trucks’ positioning (they’re designed to locate their position to within centimeters), battery charge, load content, service requirements, and other variables. The driveline and battery pack used in the cars are the same as those Volvo uses in its existing electric trucks.
You won’t see these cruising down an interstate highway, though, or even down a local highway. Vera trucks are designed to be used on short, repetitive routes contained within limited areas—think shipping ports, industrial parks, or logistics hubs. They’re limited to slower speeds than normal cars or trucks, and will be able to operate 24/7. “We will see much higher delivery precision, as well as improved flexibility and productivity,” said Mikael Karlsson, VP of Autonomous Solutions at Volvo Trucks. “Today’s operations are often designed according to standard daytime work hours, but a solution like Vera opens up the possibility of continuous round-the-clock operation and a more optimal flow. This in turn can minimize stock piles and increase overall productivity.”
The trucks are sort of like bigger versions of Amazon’s Kiva robots, which scoot around the aisles of warehouses and fulfillment centers moving pallets between shelves and fetching goods to be shipped.
Pairing trucks like Vera with robots like Kiva makes for a fascinating future landscape of logistics and transport; cargo will be moved from docks to warehouses by a large, flat robot-on-wheels, then distributed throughout that warehouse by smaller, flat robots-on-wheels. To really see the automated process through to the end point, even smaller flat robots-on-wheels will be used to deliver peoples’ goods right to their front doors.
Sounds like a lot of robots and not a lot of humans, right? Anticipating its technology’s implication in the ongoing uproar over technological unemployment, Volvo has already made statements about its intentions to continue to employ humans alongside the driverless trucks. “I foresee that there will be an increased level of automation where it makes sense, such as for repetitive tasks. This in turn will drive prosperity and increase the need for truck drivers in other applications,” said Karlsson.
The end-to-end automation concept has already been put into practice in Caofeidian, a northern Chinese city that houses the world’s first fully autonomous harbor, aiming to be operational by the end of this year. Besides replacing human-driven trucks with autonomous ones (made by Chinese startup TuSimple), the port is using automated cranes and a coordinating central control system.
Besides Uber/Otto, Tesla, or Daimler, which are all working on driverless trucks with a more conventional design (meaning they still have a cab and look like you’d expect a truck to look), Volvo also has competition from a company called Einride. The Swedish startup’s electric, cabless T/Pod looks a lot like Vera, but has some fundamental differences. Rather than being tailored to short distances and high capacity, Einride’s trucks are meant for medium distance and capacity, like moving goods from a distribution center to a series of local stores.
Vera trucks are currently still in the development phase. But since their intended use is quite specific and limited (Karlsson noted “Vera is not intended to be a solution for everyone, everywhere”), the technology could likely be rolled out faster than its more general-use counterparts. Having cabless electric trucks take over short routes in closed environments would be one more baby step along the road to a driverless future—and a testament to the fact that self-driving technology will move into our lives and our jobs incrementally, ostensibly giving us the time we’ll need to adapt and adjust.
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A research team at the University of Washington has trained an artificial intelligence system to spot obesity—all the way from space. The system used a convolutional neural network (CNN) to analyze 150,000 satellite images and look for correlations between the physical makeup of a neighborhood and the prevalence of obesity.
The team’s results, presented in JAMA Network Open, showed that features of a given neighborhood could explain close to two-thirds (64.8 percent) of the variance in obesity. Researchers found that analyzing satellite data could help increase understanding of the link between peoples’ environment and obesity prevalence. The next step would be to make corresponding structural changes in the way neighborhoods are built to encourage physical activity and better health.
Training AI to Spot Obesity
Convolutional neural networks (CNNs) are particularly adept at image analysis, object recognition, and identifying special hierarchies in large datasets.
Prior to analyzing 150,000 high-resolution satellite images of Bellevue, Seattle, Tacoma, Los Angeles, Memphis, and San Antonio, the researchers trained the CNN on 1.2 million images from the ImageNet database. The categorizations were correlated with obesity prevalence estimates for the six urban areas from census tracts gathered by the 500 Cities project.
The system was able to identify the presence of certain features that increased likelihood of obesity in a given area. Some of these features included tightly–packed houses, being close to roadways, and living in neighborhoods with a lack of greenery.
Visualization of features identified by the convolutional neural network (CNN) model. The images on the left column are satellite images taken from Google Static Maps API (application programming interface). Images in the middle and right columns are activation maps taken from the second convolutional layer of VGG-CNN-F network after forward pass of the respective satellite images through the network. From Google Static Maps API, DigitalGlobe, US Geological Survey (accessed July 2017). Credit: JAMA Network Open
Your Surroundings Are Key
In their discussion of the findings, the researchers stressed that there are limitations to the conclusions that can be drawn from the AI’s results. For example, socio-economic factors like income likely play a major role for obesity prevalence in a given geographic area.
However, the study concluded that the AI-powered analysis showed the prevalence of specific man-made features in neighborhoods consistently correlating with obesity prevalence and not necessarily correlating with socioeconomic status.
The system’s success rates varied between studied cities, with Memphis being the highest (73.3 percent) and Seattle being the lowest (55.8 percent).
AI Takes To the Sky
Around a third of the US population is categorized as obese. Obesity is linked to a number of health-related issues, and the AI-generated results could potentially help improve city planning and better target campaigns to limit obesity.
The study is one of the latest of a growing list that uses AI to analyze images and extrapolate insights.
A team at Stanford University has used a CNN to predict poverty via satellite imagery, assisting governments and NGOs to better target their efforts. A combination of the public Automatic Identification System for shipping, satellite imagery, and Google’s AI has proven able to identify illegal fishing activity. Researchers have even been able to use AI and Google Street View to predict what party a given city will vote for, based on what cars are parked on the streets.
In each case, the AI systems have been able to look at volumes of data about our world and surroundings that are beyond the capabilities of humans and extrapolate new insights. If one were to moralize about the good and bad sides of AI (new opportunities vs. potential job losses, for example) it could seem that it comes down to what we ask AI systems to look at—and what questions we ask of them.
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