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#436470 Retail Robots Are on the Rise—at Every ...
The robots are coming! The robots are coming! On our sidewalks, in our skies, in our every store… Over the next decade, robots will enter the mainstream of retail.
As countless robots work behind the scenes to stock shelves, serve customers, and deliver products to our doorstep, the speed of retail will accelerate.
These changes are already underway. In this blog, we’ll elaborate on how robots are entering the retail ecosystem.
Let’s dive in.
Robot Delivery
On August 3rd, 2016, Domino’s Pizza introduced the Domino’s Robotic Unit, or “DRU” for short. The first home delivery pizza robot, the DRU looks like a cross between R2-D2 and an oversized microwave.
LIDAR and GPS sensors help it navigate, while temperature sensors keep hot food hot and cold food cold. Already, it’s been rolled out in ten countries, including New Zealand, France, and Germany, but its August 2016 debut was critical—as it was the first time we’d seen robotic home delivery.
And it won’t be the last.
A dozen or so different delivery bots are fast entering the market. Starship Technologies, for instance, a startup created by Skype founders Janus Friis and Ahti Heinla, has a general-purpose home delivery robot. Right now, the system is an array of cameras and GPS sensors, but upcoming models will include microphones, speakers, and even the ability—via AI-driven natural language processing—to communicate with customers. Since 2016, Starship has already carried out 50,000 deliveries in over 100 cities across 20 countries.
Along similar lines, Nuro—co-founded by Jiajun Zhu, one of the engineers who helped develop Google’s self-driving car—has a miniature self-driving car of its own. Half the size of a sedan, the Nuro looks like a toaster on wheels, except with a mission. This toaster has been designed to carry cargo—about 12 bags of groceries (version 2.0 will carry 20)—which it’s been doing for select Kroger stores since 2018. Domino’s also partnered with Nuro in 2019.
As these delivery bots take to our streets, others are streaking across the sky.
Back in 2016, Amazon came first, announcing Prime Air—the e-commerce giant’s promise of drone delivery in 30 minutes or less. Almost immediately, companies ranging from 7-Eleven and Walmart to Google and Alibaba jumped on the bandwagon.
While critics remain doubtful, the head of the FAA’s drone integration department recently said that drone deliveries may be “a lot closer than […] the skeptics think. [Companies are] getting ready for full-blown operations. We’re processing their applications. I would like to move as quickly as I can.”
In-Store Robots
While delivery bots start to spare us trips to the store, those who prefer shopping the old-fashioned way—i.e., in person—also have plenty of human-robot interaction in store. In fact, these robotics solutions have been around for a while.
In 2010, SoftBank introduced Pepper, a humanoid robot capable of understanding human emotion. Pepper is cute: 4 feet tall, with a white plastic body, two black eyes, a dark slash of a mouth, and a base shaped like a mermaid’s tail. Across her chest is a touch screen to aid in communication. And there’s been a lot of communication. Pepper’s cuteness is intentional, as it matches its mission: help humans enjoy life as much as possible.
Over 12,000 Peppers have been sold. She serves ice cream in Japan, greets diners at a Pizza Hut in Singapore, and dances with customers at a Palo Alto electronics store. More importantly, Pepper’s got company.
Walmart uses shelf-stocking robots for inventory control. Best Buy uses a robo-cashier, allowing select locations to operate 24-7. And Lowe’s Home Improvement employs the LoweBot—a giant iPad on wheels—to help customers find the items they need while tracking inventory along the way.
Warehouse Bots
Yet the biggest benefit robots provide might be in-warehouse logistics.
In 2012, when Amazon dished out $775 million for Kiva Systems, few could predict that just 6 years later, 45,000 Kiva robots would be deployed at all of their fulfillment centers, helping process a whopping 306 items per second during the Christmas season.
And many other retailers are following suit.
Order jeans from the Gap, and soon they’ll be sorted, packed, and shipped with the help of a Kindred robot. Remember the old arcade game where you picked up teddy bears with a giant claw? That’s Kindred, only her claw picks up T-shirts, pants, and the like, placing them in designated drop-off zones that resemble tiny mailboxes (for further sorting or shipping).
The big deal here is democratization. Kindred’s robot is cheap and easy to deploy, allowing smaller companies to compete with giants like Amazon.
Final Thoughts
For retailers interested in staying in business, there doesn’t appear to be much choice in the way of robotics.
By 2024, the US minimum wage is projected to be $15 an hour (the House of Representatives has already passed the bill, but the wage hike is meant to unfold gradually between now and 2025), and many consider that number far too low.
Yet, as human labor costs continue to climb, robots won’t just be coming, they’ll be here, there, and everywhere. It’s going to become increasingly difficult for store owners to justify human workers who call in sick, show up late, and can easily get injured. Robots work 24-7. They never take a day off, never need a bathroom break, health insurance, or parental leave.
Going forward, this spells a growing challenge of technological unemployment (a blog topic I will cover in the coming month). But in retail, robotics usher in tremendous benefits for companies and customers alike.
And while professional re-tooling initiatives and the transition of human capital from retail logistics to a booming experience economy take hold, robotic retail interaction and last-mile delivery will fundamentally transform our relationship with commerce.
This blog comes from The Future is Faster Than You Think—my upcoming book, to be released Jan 28th, 2020. To get an early copy and access up to $800 worth of pre-launch giveaways, sign up here!
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Posted in Human Robots
#436466 How Two Robots Learned to Grill and ...
The list of things robots can do seems to be growing by the week. They can play sports, help us explore outer space and the deep sea, take over some of our boring everyday tasks, and even assemble Ikea furniture.
Now they can add one more accomplishment to the list: grilling and serving a hot dog.
It seems like a pretty straightforward task, and as far as grilling goes, hot dogs are about as easy as it gets (along with, maybe, burgers? Hot dogs require more rotation, but it’s easier to tell when they’re done since they’re lighter in color).
Let’s paint a picture: you’re manning the grill at your family’s annual Fourth of July celebration. You’ve got a 10-pack of plump, juicy beef franks and a hungry crowd of relatives whose food-to-alcohol ratio is getting pretty skewed—they need some solid calories, pronto. What are the steps you need to take to get those franks from package to plate?
Each one needs to be placed on the grill, rotated every couple minutes for even cooking, removed from the grill when you deem it’s done, then—if you’re the kind of guy or gal who goes the extra mile—placed in a bun and dressed with ketchup, mustard, pickles, and the like before being handed over to salivating, too-loud Uncle Hector or sweet, bored Cousin Margaret.
While carrying out your grillmaster duties, you know better than to drop the hot dogs on the ground, leave them cooking on one side for too long, squeeze them to the point of breaking or bursting, and any other hot-dog-ruining amateur moves.
But for a robot, that’s a lot to figure out, especially if they have no prior knowledge of grilling hot dogs (which, well, most robots don’t).
As described in a paper published in this week’s Science Robotics, a team from Boston University programmed two robotic arms to use reinforcement learning—a branch of machine learning in which software gathers information about its environment then learns from it by replaying its experiences and incorporating rewards—to cook and serve hot dogs.
The team used a set of formulas to specify and combine tasks (“pick up hot dog and place on the grill”), meet safety requirements (“always avoid collisions”), and incorporate general prior knowledge (“you cannot pick up another hot dog if you are already holding one”).
Baxter and Jaco—as the two robots were dubbed—were trained through computer simulations. The paper’s authors emphasized their use of what they call a “formal specification language” for training the software, with the aim of generating easily-interpretable task descriptions. In reinforcement learning, they explain, being able to understand how a reward function influences an AI’s learning process is a key component in understanding the system’s behavior—but most systems lack this quality, and are thus likely to be lumped into the ‘black box’ of AI.
The robots’ decisions throughout the hot dog prep process—when to turn a hot dog, when to take it off the grill, and so on—are, the authors write, “easily interpretable from the beginning because the language is very similar to plain English.”
Besides being a step towards more explainable AI systems, Baxter and Jaco are another example of fast-food robots—following in the footsteps of their burger and pizza counterparts—that may take over some repetitive manual tasks currently performed by human workers. As robots’ capabilities improve through incremental progress like this, they’ll be able to take on additional tasks.
In a not-so-distant future, then, you just may find yourself throwing back drinks with Uncle Hector and Cousin Margaret while your robotic replacement mans the grill, churning out hot dogs that are perfectly cooked every time.
Image Credit: Image by Muhammad Ribkhan from Pixabay Continue reading
Posted in Human Robots
#436426 Video Friday: This Robot Refuses to Fall ...
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):
Robotic Arena – January 25, 2020 – Wrocław, Poland
DARPA SubT Urban Circuit – February 18-27, 2020 – Olympia, Wash., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.
In case you somehow missed the massive Skydio 2 review we posted earlier this week, the first batches of the drone are now shipping. Each drone gets a lot of attention before it goes out the door, and here’s a behind-the-scenes clip of the process.
[ Skydio ]
Sphero RVR is one of the 15 robots on our robot gift guide this year. Here’s a new video Sphero just released showing some of the things you can do with the robot.
[ RVR ]
NimbRo-OP2 has some impressive recovery skills from the obligatory research-motivated robot abuse.
[ NimbRo ]
Teams seeking to qualify for the Virtual Urban Circuit of the Subterranean Challenge can access practice worlds to test their approaches prior to submitting solutions for the competition. This video previews three of the practice environments.
[ DARPA SubT ]
Stretchable skin-like robots that can be rolled up and put in your pocket have been developed by a University of Bristol team using a new way of embedding artificial muscles and electrical adhesion into soft materials.
[ Bristol ]
Happy Holidays from ABB!
Helping New York celebrate the festive season, twelve ABB robots are interacting with visitors to Bloomingdale’s iconic holiday celebration at their 59th Street flagship store. ABB’s robots are the main attraction in three of Bloomingdale’s twelve-holiday window displays at Lexington and Third Avenue, as ABB demonstrates the potential for its robotics and automation technology to revolutionize visual merchandising and make the retail experience more dynamic and whimsical.
[ ABB ]
We introduce pelican eel–inspired dual-morphing architectures that embody quasi-sequential behaviors of origami unfolding and skin stretching in response to fluid pressure. In the proposed system, fluid paths were enclosed and guided by a set of entirely stretchable origami units that imitate the morphing principle of the pelican eel’s stretchable and foldable frames. This geometric and elastomeric design of fluid networks, in which fluid pressure acts in the direction that the whole body deploys first, resulted in a quasi-sequential dual-morphing response. To verify the effectiveness of our design rule, we built an artificial creature mimicking a pelican eel and reproduced biomimetic dual-morphing behavior.
And here’s a real pelican eel:
[ Science Robotics ]
Delft Dynamics’ updated anti-drone system involves a tether, mid-air net gun, and even a parachute.
[ Delft Dynamics ]
Teleoperation is a great way of helping robots with complex tasks, especially if you can do it through motion capture. But what if you’re teleoperating a non-anthropomorphic robot? Columbia’s ROAM Lab is working on it.
[ Paper ] via [ ROAM Lab ]
I don’t know how I missed this video last year because it’s got a steely robot hand squeezing a cute lil’ chick.
[ MotionLib ] via [ RobotStart ]
In this video we present results of a trajectory generation method for autonomous overtaking of unexpected obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example when overtaking unexpected objects on the vehicle’s ego lane on a two-way street. In this case, a human driver would first make sure that the opposite lane is free and that there is enough room to successfully execute the maneuver, and then it would cut into the opposite lane in order to execute the maneuver successfully. We consider the practical problem of autonomous overtaking when the coverage of the perception system is impaired due to occlusion.
[ Paper ]
New weirdness from Toio!
[ Toio ]
Palo Alto City Library won a technology innovation award! Watch to see how Senior Librarian Dan Lou is using Misty to enhance their technology programs to inspire and educate customers.
[ Misty Robotics ]
We consider the problem of reorienting a rigid object with arbitrary known shape on a table using a two-finger pinch gripper. Reorienting problem is challenging because of its non-smoothness and high dimensionality. In this work, we focus on solving reorienting using pivoting, in which we allow the grasped object to rotate between fingers. Pivoting decouples the gripper rotation from the object motion, making it possible to reorient an object under strict robot workspace constraints.
[ CMU ]
How can a mobile robot be a good pedestrian without bumping into you on the sidewalk? It must be hard for a robot to navigate in crowded environments since the flow of traffic follows implied social rules. But researchers from MIT developed an algorithm that teaches mobile robots to maneuver in crowds of people, respecting their natural behaviour.
[ Roboy Research Reviews ]
What happens when humans and robots make art together? In this awe-inspiring talk, artist Sougwen Chung shows how she “taught” her artistic style to a machine — and shares the results of their collaboration after making an unexpected discovery: robots make mistakes, too. “Part of the beauty of human and machine systems is their inherent, shared fallibility,” she says.
[ TED ]
Last month at the Cooper Union in New York City, IEEE TechEthics hosted a public panel session on the facts and misperceptions of autonomous vehicles, part of the IEEE TechEthics Conversations Series. The speakers were: Jason Borenstein from Georgia Tech; Missy Cummings from Duke University; Jack Pokrzywa from SAE; and Heather M. Roff from Johns Hopkins Applied Physics Laboratory. The panel was moderated by Mark A. Vasquez, program manager for IEEE TechEthics.
[ IEEE TechEthics ]
Two videos this week from Lex Fridman’s AI podcast: Noam Chomsky, and Whitney Cummings.
[ AI Podcast ]
This week’s CMU RI Seminar comes from Jeff Clune at the University of Wyoming, on “Improving Robot and Deep Reinforcement Learning via Quality Diversity and Open-Ended Algorithms.”
Quality Diversity (QD) algorithms are those that seek to produce a diverse set of high-performing solutions to problems. I will describe them and a number of their positive attributes. I will then summarize our Nature paper on how they, when combined with Bayesian Optimization, produce a learning algorithm that enables robots, after being damaged, to adapt in 1-2 minutes in order to continue performing their mission, yielding state-of-the-art robot damage recovery. I will next describe our QD-based Go-Explore algorithm, which dramatically improves the ability of deep reinforcement learning algorithms to solve previously unsolvable problems wherein reward signals are sparse, meaning that intelligent exploration is required. Go-Explore solves Montezuma’s Revenge, considered by many to be a major AI research challenge. Finally, I will motivate research into open-ended algorithms, which seek to innovate endlessly, and introduce our POET algorithm, which generates its own training challenges while learning to solve them, automatically creating a curricula for robots to learn an expanding set of diverse skills. POET creates and solves challenges that are unsolvable with traditional deep reinforcement learning techniques.
[ CMU RI ] Continue reading
Posted in Human Robots
#436403 Why Your 5G Phone Connection Could Mean ...
Will getting full bars on your 5G connection mean getting caught out by sudden weather changes?
The question may strike you as hypothetical, nonsensical even, but it is at the core of ongoing disputes between meteorologists and telecommunications companies. Everyone else, including you and I, are caught in the middle, wanting both 5G’s faster connection speeds and precise information about our increasingly unpredictable weather. So why can’t we have both?
Perhaps we can, but because of the way 5G networks function, it may take some special technology—specifically, artificial intelligence.
The Bandwidth Worries
Around the world, the first 5G networks are already being rolled out. The networks use a variety of frequencies to transmit data to and from devices at speeds up to 100 times faster than existing 4G networks.
One of the bandwidths used is between 24.25 and 24.45 gigahertz (GHz). In a recent FCC auction, telecommunications companies paid a combined $2 billion for the 5G usage rights for this spectrum in the US.
However, meteorologists are concerned that transmissions near the lower end of that range can interfere with their ability to accurately measure water vapor in the atmosphere. Wired reported that acting chief of the National Oceanic and Atmospheric Administration (NOAA), Neil Jacobs, told the US House Subcommittee on the Environment that 5G interference could substantially cut the amount of weather data satellites can gather. As a result, forecast accuracy could drop by as much as 30 percent.
Among the consequences could be less time to prepare for hurricanes, and it may become harder to predict storms’ paths. Due to the interconnectedness of weather patterns, measurement issues in one location can affect other areas too. Lack of accurate atmospheric data from the US could, for example, lead to less accurate forecasts for weather patterns over Europe.
The Numbers Game
Water vapor emits a faint signal at 23.8 GHz. Weather satellites measure the signals, and the data is used to gauge atmospheric humidity levels. Meteorologists have expressed concern that 5G signals in the same range can disturb those readings. The issue is that it would be nigh on impossible to tell whether a signal is water vapor or an errant 5G signal.
Furthermore, 5G disturbances in other frequency bands could make forecasting even more difficult. Rain and snow emit frequencies around 36-37 GHz. 50.2-50.4 GHz is used to measure atmospheric temperatures, and 86-92 GHz clouds and ice. All of the above are under consideration for international 5G signals. Some have warned that the wider consequences could set weather forecasts back to the 1980s.
Telecommunications companies and interest organizations have argued back, saying that weather sensors aren’t as susceptible to interference as meteorologists fear. Furthermore, 5G devices and signals will produce much less interference with weather forecasts than organizations like NOAA predict. Since very little scientific research has been carried out to examine the claims of either party, we seem stuck in a ‘wait and see’ situation.
To offset some of the possible effects, the two groups have tried to reach a consensus on a noise buffer between the 5G transmissions and water-vapor signals. It could be likened to limiting the noise from busy roads or loud sound systems to avoid bothering neighboring buildings.
The World Meteorological Organization was looking to establish a -55 decibel watts buffer. In Europe, regulators are locked in on a -42 decibel watts buffer for 5G base stations. For comparison, the US Federal Communications Commission has advocated for a -20 decibel watts buffer, which would, in reality, allow more than 150 times more noise than the European proposal.
How AI Could Help
Much of the conversation about 5G’s possible influence on future weather predictions is centered around mobile phones. However, the phones are far from the only systems that will be receiving and transmitting signals on 5G. Self-driving cars and the Internet of Things are two other technologies that could soon be heavily reliant on faster wireless signals.
Densely populated areas are likely going to be the biggest emitters of 5G signals, leading to a suggestion to only gather water-vapor data over oceans.
Another option is to develop artificial intelligence (AI) approaches to clean or process weather data. AI is playing an increasing role in weather forecasting. For example, in 2016 IBM bought The Weather Company for $2 billion. The goal was to combine the two companies’ models and data in IBM’s Watson to create more accurate forecasts. AI would also be able to predict increases or drops in business revenues due to weather changes. Monsanto has also been investing in AI for forecasting, in this case to provide agriculturally-related weather predictions.
Smartphones may also provide a piece of the weather forecasting puzzle. Studies have shown how data from thousands of smartphones can help to increase the accuracy of storm predictions, as well as the force of storms.
“Weather stations cost a lot of money,” Cliff Mass, an atmospheric scientist at the University of Washington in Seattle, told Inside Science, adding, “If there are already 20 million smartphones, you might as well take advantage of the observation system that’s already in place.”
Smartphones may not be the solution when it comes to finding new ways of gathering the atmospheric data on water vapor that 5G could disrupt. But it does go to show that some technologies open new doors, while at the same time, others shut them.
Image Credit: Image by Free-Photos from Pixabay Continue reading
Posted in Human Robots