Tag Archives: sensors

#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

#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

#435779 This Robot Ostrich Can Ride Around on ...

Posted on September 20, 2019 by Android

Proponents of legged robots say that they make sense because legs are often required to go where humans go. Proponents of wheeled robots say, “Yeah, that’s great but watch how fast and efficient my robot is, compared to yours.” Some robots try and take advantage of wheels and legs with hybrid designs like whegs or wheeled feet, but a simpler and more versatile solution is to do what humans do, and just take advantage of wheels when you need them.

We’ve seen a few experiments with this. The University of Michigan managed to convince Cassie to ride a Segway, with mostly positive (but occasionally quite negative) results. A Segway, and hoverboard-like systems, can provide wheeled mobility for legged robots over flat terrain, but they can’t handle things like stairs, which is kind of the whole point of having a robot with legs anyway.

Image: UC Berkeley

From left, a Segway, a hovercraft, and hovershoes, with complexity in terms of user control increasing from left to right.

At UC Berkeley’s Hybrid Robotics Lab, led by Koushil Sreenath, researchers have taken things a step further. They are teaching their Cassie bipedal robot (called Cassie Cal) to wheel around on a pair of hovershoes. Hovershoes are like hoverboards that have been chopped in half, resulting in a pair of motorized single-wheel skates. You balance on the skates, and control them by leaning forwards and backwards and left and right, which causes each skate to accelerate or decelerate in an attempt to keep itself upright. It’s not easy to get these things to work, even for a human, but by adding a sensor package to Cassie the UC Berkeley researchers have managed to get it to zip around campus fully autonomously.

Remember, Cassie is operating autonomously here—it’s performing vSLAM (with an Intel RealSense) and doing all of its own computation onboard in real time. Watching it jolt across that cracked sidewalk is particularly impressive, especially considering that it only has pitch control over its ankles and can’t roll its feet to maintain maximum contact with the hovershoes. But you can see the advantage that this particular platform offers to a robot like Cassie, including the ability to handle stairs. Stairs in one direction, anyway.

It’s a testament to the robustness of UC Berkeley’s controller that they were willing to let the robot operate untethered and outside, and it sounds like they’re thinking long-term about how legged robots on wheels would be real-world useful:

Our feedback control and autonomous system allow for swift movement through urban environments to aid in everything from food delivery to security and surveillance to search and rescue missions. This work can also help with transportation in large factories and warehouses.

For more details, we spoke with the UC Berkeley students (Shuxiao Chen, Jonathan Rogers, and Bike Zhang) via email.

IEEE Spectrum: How representative of Cassie’s real-world performance is what we see in the video? What happens when things go wrong?

Cassie’s real-world performance is similar to what we see in the video. Cassie can ride the hovershoes successfully all around the campus. Our current controller allows Cassie to robustly ride the hovershoes and rejects various perturbations. At present, one of the failure modes is when the hovershoe rolls to the side—this happens when it goes sideways down a step or encounters a large obstacle on one side of it, causing it to roll over. Under these circumstances, Cassie doesn’t have sufficient control authority (due to the thin narrow feet) to get the hovershoe back on its wheel.

The Hybrid Robotics Lab has been working on robots that walk over challenging terrain—how do wheeled platforms like hovershoes fit in with that?

Surprisingly, this research is related to our prior work on walking on discrete terrain. While locomotion using legs is efficient when traveling over rough and discrete terrain, wheeled locomotion is more efficient when traveling over flat continuous terrain. Enabling legged robots to ride on various micro-mobility platforms will offer multimodal locomotion capabilities, improving the efficiency of locomotion over various terrains.

Our current research furthers the locomotion ability for bipedal robots over continuous terrains by using a wheeled platform. In the long run, we would like to develop multi-modal locomotion strategies based on our current and prior work to allow legged robots to robustly and efficiently locomote in our daily life.

Photo: UC Berkeley

In their experiments, the UC Berkeley researchers say Cassie proved quite capable of riding the hovershoes over rough and uneven terrain, including going down stairs.

How long did it take to train Cassie to use the hovershoes? Are there any hovershoe skills that Cassie is better at than an average human?

We spent about eight months to develop our whole system, including a controller, a path planner, and a vision system. This involved developing mathematical models of Cassie and the hovershoes, setting up a dynamical simulation, figuring out how to interface and communicate with various sensors and Cassie, and doing several experiments to slowly improve performance. In contrast, a human with a good sense of balance needs a few hours to learn to use the hovershoes. A human who has never used skates or skis will probably need a longer time.

A human can easily turn in place on the hovershoes, while Cassie cannot do this motion currently due to our algorithm requiring a non-zero forward speed in order to turn. However, Cassie is much better at riding the hovershoes over rough and uneven terrain including riding the hovershoes down some stairs!

What would it take to make Cassie faster or more agile on the hovershoes?

While Cassie can currently move at a decent pace on the hovershoes and navigate obstacles, Cassie’s ability to avoid obstacles at rapid speeds is constrained by the sensing, the controller, and the onboard computation. To enable Cassie to dynamically weave around obstacles at high speeds exhibiting agile motions, we need to make progress on different fronts.

We need planners that take into account the entire dynamics of the Cassie-Hovershoe system and rapidly generate dynamically-feasible trajectories; we need controllers that tightly coordinate all the degrees-of-freedom of Cassie to dynamically move while balancing on the hovershoes; we need sensors that are robust to motion-blur artifacts caused due to fast turns; and we need onboard computation that can execute our algorithms at real-time speeds.

What are you working on next?

We are working on enabling more aggressive movements for Cassie on the hovershoes by fully exploiting Cassie’s dynamics. We are working on approaches that enable us to easily go beyond hovershoes to other challenging micro-mobility platforms. We are working on enabling Cassie to step onto and off from wheeled platforms such as hovershoes. We would like to create a future of multi-modal locomotion strategies for legged robots to enable them to efficiently help people in our daily life.

“Feedback Control for Autonomous Riding of Hovershoes by a Cassie Bipedal Robot,” by Shuxiao Chen, Jonathan Rogers, Bike Zhang, and Koushil Sreenath from the Hybrid Robotics Lab at UC Berkeley, has been submitted to IEEE Robotics and Automation Letters with option to be presented at the 2019 IEEE RAS International Conference on Humanoid Robots. Continue reading →

Posted in Human Robots

#435773 Video Friday: Roller-Skating Quadruped ...

Posted on September 20, 2019 by Android

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!):

IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
RoboBusiness 2019 – October 1-3, 2019 – Santa Clara, CA, USA
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today's videos.

We got a sneak peek of a new version of ANYmal equipped with actuated wheels for feet at the DARPA SubT Challenge, where it did surprisingly well at quickly and (mostly) robustly navigating some very tricky terrain. And when you're not expecting it to travel through a muddy, rocky, and dark tunnel, it looks even more capable:

[ Paper ]

Thanks Marko!

In Langley’s makerspace lab, researchers are developing a series of soft robot actuators to investigate the viability of soft robotics in space exploration and assembly. By design, the actuator has chambers, or air bladders, that expand and compress based on the amount of air in them.

[ NASA ]

I’m not normally a fan of the AdultSize RoboCup soccer competition, but NimbRo had a very impressive season.

I don’t know how it managed to not fall over at 45 seconds, but damn.

[ NimbRo ]

This is more AI than robotics, but that’s okay, because it’s totally cool.

I’m wondering whether the hiders ever tried another possibly effective strategy: trapping the seekers in a locked shelter right at the start.

[ OpenAI ]

We haven’t heard much from Piaggio Fast Forward in a while, but evidently they’ve still got a Gita robot going on, designed to be your personal autonomous caddy for absolutely anything that can fit into something the size of a portable cooler.

Available this fall, I guess?

[ Gita ]

This passively triggered robotic hand is startlingly fast, and seems almost predatory when it grabs stuff, especially once they fit it onto a drone.

[ New Dexterity ]

Thanks Fan!

Autonomous vehicles seem like a recent thing, but CMU has been working on them since the mid 1980s.

CMU was also working on drones back before drones were even really a thing:

[ CMU NavLab ] and [ CMU ]

Welcome to the most complicated and expensive robotic ice cream deployment system ever created.

[ Niska ]

Some impressive dexterity from a robot hand equipped with magnetic gears.

[ Ishikawa Senoo Lab ]

The Buddy Arduino social robot kit is now live on Kickstarter, and you can pledge for one of these little dudes for 49 bucks.

[ Kickstarter ]

Thanks Jenny!

Mobile manipulation robots have high potential to support rescue forces in disaster-response missions. Despite the difficulties imposed by real-world scenarios, robots are promising to perform mission tasks from a safe distance. In the CENTAURO project, we developed a disaster-response system which consists of the highly flexible Centauro robot and suitable control interfaces including an immersive telepresence suit and support-operator controls on different levels of autonomy.

[ CENTAURO ]

Thanks Sven!

Determined robots are the cutest robots.

[ Paper ]

The goal of the Dronument project is to create an aerial platform enabling interior and exterior documentation of heritage sites.

It’s got a base station that helps with localization, but still, flying that close to a chandelier in a UNESCO world heritage site makes me nervous.

[ Dronument ]

Thanks Fan!

Avast ye! No hornswaggling, lick-spittlering, or run-rigging over here – Only serious tech for devs. All hands hoay to check out Misty's capabilities and to build your own skills with plenty of heave ho! ARRRRRRRRGH…

International Talk Like a Pirate Day was yesterday, but I'm sure nobody will look at you funny if you keep at it today too.

[ Misty Robotics ]

This video presents an unobtrusive bimanual teleoperation setup with very low weight, consisting of two Vive visual motion trackers and two Myo surface electromyography bracelets. The video demonstrates complex, dexterous teleoperated bimanual daily-living tasks performed by the torque-controlled humanoid robot TORO.

[ DLR RMC ]

Lex Fridman interviews iRobot’s Colin Angle on the Artificial Intelligence Podcast.

Colin Angle is the CEO and co-founder of iRobot, a robotics company that for 29 years has been creating robots that operate successfully in the real world, not as a demo or on a scale of dozens, but on a scale of thousands and millions. As of this year, iRobot has sold more than 25 million robots to consumers, including the Roomba vacuum cleaning robot, the Braava floor mopping robot, and soon the Terra lawn mowing robot. 25 million robots successfully operating autonomously in people's homes to me is an incredible accomplishment of science, engineering, logistics, and all kinds of entrepreneurial innovation.

[ AI Podcast ]

This week’s CMU RI Seminar comes from CMU’s own Sarah Bergbreiter, on Microsystems-Inspired Robotics.

The ability to manufacture micro-scale sensors and actuators has inspired the robotics community for over 30 years. There have been huge success stories; MEMS inertial sensors have enabled an entire market of low-cost, small UAVs. However, the promise of ant-scale robots has largely failed. Ants can move high speeds on surfaces from picnic tables to front lawns, but the few legged microrobots that have walked have done so at slow speeds (< 1 body length/sec) on smooth silicon wafers. In addition, the vision of large numbers of microfabricated sensors interacting directly with the environment has suffered in part due to the brittle materials used in micro-fabrication. This talk will present our progress in the design of sensors, mechanisms, and actuators that utilize new microfabrication processes to incorporate materials with widely varying moduli and functionality to achieve more robustness, dynamic range, and complexity in smaller packages.

[ CMU RI ] Continue reading →

Posted in Human Robots

#435769 The Ultimate Optimization Problem: How ...

Posted on September 20, 2019 by Android

Lucas Joppa thinks big. Even while gazing down into his cup of tea in his modest office on Microsoft’s campus in Redmond, Washington, he seems to see the entire planet bobbing in there like a spherical tea bag.

As Microsoft’s first chief environmental officer, Joppa came up with the company’s AI for Earth program, a five-year effort that’s spending US $50 million on AI-powered solutions to global environmental challenges.

The program is not just about specific deliverables, though. It’s also about mindset, Joppa told IEEE Spectrum in an interview in July. “It’s a plea for people to think about the Earth in the same way they think about the technologies they’re developing,” he says. “You start with an objective. So what’s our objective function for Earth?” (In computer science, an objective function describes the parameter or parameters you are trying to maximize or minimize for optimal results.)

Photo: Microsoft

Lucas Joppa

AI for Earth launched in December 2017, and Joppa’s team has since given grants to more than 400 organizations around the world. In addition to receiving funding, some grantees get help from Microsoft’s data scientists and access to the company’s computing resources.

In a wide-ranging interview about the program, Joppa described his vision of the “ultimate optimization problem”—figuring out which parts of the planet should be used for farming, cities, wilderness reserves, energy production, and so on.

Every square meter of land and water on Earth has an infinite number of possible utility functions. It’s the job of Homo sapiens to describe our overall objective for the Earth. Then it’s the job of computers to produce optimization results that are aligned with the human-defined objective.

I don’t think we’re close at all to being able to do this. I think we’re closer from a technology perspective—being able to run the model—than we are from a social perspective—being able to make decisions about what the objective should be. What do we want to do with the Earth’s surface?

Such questions are increasingly urgent, as climate change has already begun reshaping our planet and our societies. Global sea and air surface temperatures have already risen by an average of 1 degree Celsius above preindustrial levels, according to the Intergovernmental Panel on Climate Change.

Today, people all around the world participated in a “climate strike,” with young people leading the charge and demanding a global transition to renewable energy. On Monday, world leaders will gather in New York for the United Nations Climate Action Summit, where they’re expected to present plans to limit warming to 1.5 degrees Celsius.

Joppa says such summit discussions should aim for a truly holistic solution.

We talk about how to solve climate change. There’s a higher-order question for society: What climate do we want? What output from nature do we want and desire? If we could agree on those things, we could put systems in place for optimizing our environment accordingly. Instead we have this scattered approach, where we try for local optimization. But the sum of local optimizations is never a global optimization.

There’s increasing interest in using artificial intelligence to tackle global environmental problems. New sensing technologies enable scientists to collect unprecedented amounts of data about the planet and its denizens, and AI tools are becoming vital for interpreting all that data.

The 2018 report “Harnessing AI for the Earth,” produced by the World Economic Forum and the consulting company PwC, discusses ways that AI can be used to address six of the world’s most pressing environmental challenges (climate change, biodiversity, and healthy oceans, water security, clean air, and disaster resilience).

Many of the proposed applications involve better monitoring of human and natural systems, as well as modeling applications that would enable better predictions and more efficient use of natural resources.

Joppa says that AI for Earth is taking a two-pronged approach, funding efforts to collect and interpret vast amounts of data alongside efforts that use that data to help humans make better decisions. And that’s where the global optimization engine would really come in handy.

For any location on earth, you should be able to go and ask: What’s there, how much is there, and how is it changing? And more importantly: What should be there?

On land, the data is really only interesting for the first few hundred feet. Whereas in the ocean, the depth dimension is really important.

We need a planet with sensors, with roving agents, with remote sensing. Otherwise our decisions aren’t going to be any good.

AI for Earth isn’t going to create such an online portal within five years, Joppa stresses. But he hopes the projects that he’s funding will contribute to making such a portal possible—eventually.

We’re asking ourselves: What are the fundamental missing layers in the tech stack that would allow people to build a global optimization engine? Some of them are clear, some are still opaque to me.

By the end of five years, I’d like to have identified these missing layers, and have at least one example of each of the components.

Some of the projects that AI for Earth has funded seem to fit that desire. Examples include SilviaTerra, which used satellite imagery and AI to create a map of the 92 billion trees in forested areas across the United States. There’s also OceanMind, a non-profit that detects illegal fishing and helps marine authorities enforce compliance. Platforms like Wildbook and iNaturalist enable citizen scientists to upload pictures of animals and plants, aiding conservation efforts and research on biodiversity. And FarmBeats aims to enable data-driven agriculture with low-cost sensors, drones, and cloud services.

It’s not impossible to imagine putting such services together into an optimization engine that knows everything about the land, the water, and the creatures who live on planet Earth. Then we’ll just have to tell that engine what we want to do about it.

Editor’s note: This story is published in cooperation with more than 250 media organizations and independent journalists that have focused their coverage on climate change ahead of the UN Climate Action Summit. IEEE Spectrum’s participation in the Covering Climate Now partnership builds on our past reporting about this global issue. Continue reading →

Posted in Human Robots