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Robot technology is evolving at breakneck speed. SoftBank’s Pepper is found in companies across the globe and is rapidly improving its conversation skills. Telepresence robots open up new opportunities for remote working, while Boston Dynamics’ Handle robot could soon (literally) take a load off human colleagues in warehouses.
But warehouses and offices aren’t the only places where robots are lining up next to humans.
Toyota’s Cue 3 robot recently showed off its basketball skills, putting up better numbers than the NBA’s most accurate three-point shooter, the Golden State Warriors’ Steph Curry.
Cue 3 is still some way from being ready to take on Curry, or even amateur basketball players, in a real game. However, it is the latest member of a growing cast of robots challenging human dominance in sports.
As these robots continue to develop, they not only exemplify the speed of exponential technology development, but also how those technologies are improving human capabilities.
Meet the Contestants
The list of robots in sports is surprisingly long and diverse. There are robot skiers, tumblers, soccer players, sumos, and even robot game jockeys. Introductions to a few of them are in order.
Sport: Table tennis
Intro: Looks like something out of War of the Worlds equipped with a ping pong bat instead of a death ray.
Ability level: Capable of counteracting spin shots and good enough to beat many beginners.
Robot: Sumo bot
Sport: Sumo wrestling
Intro: Hyper-fast, hyper-aggressive. Think robot equivalent to an angry wasp on six cans of Red Bull crossed with a very small tank.
Ability level: Flies around the ring way faster than any human sumo. Tend to drive straight out of the ring at times.
Robot: Cue 3
Intro: Stands at an imposing 6 foot and 10 inches, so pretty much built for the NBA. Looks a bit like something that belongs in a video game.
Ability level: A 62.5 percent three-pointer percentage, which is better than Steph Curry’s; is less mobile than Charles Barkley – in his current form.
Robot: Robo Cup Robots
Intro: The future of soccer. If everything goes to plan, a team of robots will take on the Lionel Messis and Cristiano Ronaldos of 2050 and beat them in a full 11 vs. 11 game.
Ability level: Currently plays soccer more like the six-year-olds I used to coach than Lionel Messi.
The Limiting Factor
The skill level of all the robots above is impressive, and they are doing things that no human contestant can. The sumo bots’ inhuman speed is self-evident. Forpheus’ ability to track the ball with two cameras while simultaneously tracking its opponent with two other cameras requires a look at the spec sheet, but is similarly beyond human capability. While Cue 3 can’t move, it makes shots from the mid-court logo look easy.
Robots are performing at a level that was confined to the realm of science fiction at the start of the millennium. The speed of development indicates that in the near future, my national team soccer coach would likely call up a robot instead of me (he must have lost my number since he hasn’t done so yet. It’s the only logical explanation), and he’d be right to do so.
It is also worth considering that many current sports robots have a humanoid form, which limits their ability. If engineers were to optimize robot design to outperform humans in specific categories, many world champions would likely already be metallic.
Swimming is perhaps one of the most obvious. Even Michael Phelps would struggle to keep up with a torpedo-shaped robot, and if you beefed up a sumo robot to human size, human sumos might impress you by running away from them with a 100-meter speed close to Usain Bolt’s.
In other areas, the playing field for humans and robots is rapidly leveling. One likely candidate for the first head-to-head competitions is racing, where self-driving cars from the Roborace League could perhaps soon be ready to race the likes of Lewis Hamilton.
Tech Pushing Humans
Perhaps one of the biggest reasons why it may still take some time for robots to surpass us is that they, along with other exponential technologies, are already making us better at sports.
In Japan, elite volleyball players use a robot to practice their attacks. Some American football players also practice against robot opponents and hone their skills using VR.
On the sidelines, AI is being used to analyze and improve athletes’ performance, and we may soon see the first AI coaches, not to mention referees.
We may even compete in games dreamt up by our electronic cousins. SpeedGate, a new game created by an AI by studying 400 different sports, is a prime example of that quickly becoming a possibility.
However, we will likely still need to make the final call on what constitutes a good game. The AI that created SpeedGate reportedly also suggested less suitable pastimes, like underwater parkour and a game that featured exploding frisbees. Both of these could be fun…but only if you’re as sturdy as a robot.
Image Credit: RoboCup Standard Platform League 2018, ©The Robocup Federation. Published with permission of reproduction granted by the RoboCup Federation. Continue reading
Open AI’s Dota 2 AI Steamrolls World Champion e-Sports Team With Back-to-Back Victories
Nick Statt | The Verge
“…[OpenAI cofounder and CEO, Sam Altman] tells me there probably does not exist a video game out there right now that a system like OpenAI Five can’t eventually master at a level beyond human capability. For the broader AI industry, mastering video games may soon become passé, simple table stakes required to prove your system can learn fast and act in a way required to tackle tougher, real-world tasks with more meaningful benefits.”
Boston Dynamics Debuts the Production Version of SpotMini
Brian Heater, Catherine Shu | TechCrunch
“SpotMini is the first commercial robot Boston Dynamics is set to release, but as we learned earlier, it certainly won’t be the last. The company is looking to its wheeled Handle robot in an effort to push into the logistics space. It’s a super-hot category for robotics right now. Notably, Amazon recently acquired Colorado-based start up Canvas to add to its own arm of fulfillment center robots.”
Scientists Restore Some Brain Cell Functions in Pigs Four Hours After Death
Joel Achenbach | The Washington Post
“The ethicists say this research can blur the line between life and death, and could complicate the protocols for organ donation, which rely on a clear determination of when a person is dead and beyond resuscitation.”
How Scientists 3D Printed a Tiny Heart From Human Cells
Yasmin Saplakoglu | Live Science
“Though the heart is much smaller than a human’s (it’s only the size of a rabbit’s), and there’s still a long way to go until it functions like a normal heart, the proof-of-concept experiment could eventually lead to personalized organs or tissues that could be used in the human body…”
The Next Clash of Silicon Valley Titans Will Take Place in Space
Luke Dormehl | Digital Trends
“With bold plans that call for thousands of new satellites being put into orbit and astronomical costs, it’s going to be fascinating to observe the next phase of the tech platform battle being fought not on our desktops or mobile devices in our pockets, but outside of Earth’s atmosphere.”
The Images That Could Help Rebuild Notre-Dame Cathedral
Alexis C. Madrigal | The Atlantic
“…in 2010, [Andrew] Tallon, an art professor at Vassar, took a Leica ScanStation C10 to Notre-Dame and, with the assistance of Columbia’s Paul Blaer, began to painstakingly scan every piece of the structure, inside and out. …Over five days, they positioned the scanner again and again—50 times in all—to create an unmatched record of the reality of one of the world’s most awe-inspiring buildings, represented as a series of points in space.”
Mapping Our World in 3D Will Let Us Paint Streets With Augmented Reality
Charlotte Jee | MIT Technology Review
“Scape wants to use its location services to become the underlying infrastructure upon which driverless cars, robotics, and augmented-reality services sit. ‘Our end goal is a one-to-one map of the world covering everything,’ says Miller. ‘Our ambition is to be as invisible as GPS is today.’i”
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Robots have been masters of manufacturing at speed and precision for decades, but give them a seemingly simple task like stacking shelves, and they quickly get stuck. That’s changing, though, as engineers build systems that can take on the deceptively tricky tasks most humans can do with their eyes closed.
Boston Dynamics is famous for dramatic reveals of robots performing mind-blowing feats that also leave you scratching your head as to what the market is—think the bipedal Atlas doing backflips or Spot the galloping robot dog.
Last week, the company released a video of a robot called Handle that looks like an ostrich on wheels carrying out the seemingly mundane task of stacking boxes in a warehouse.
It might seem like a step backward, but this is exactly the kind of practical task robots have long struggled with. While the speed and precision of industrial robots has seen them take over many functions in modern factories, they’re generally limited to highly prescribed tasks carried out in meticulously-controlled environments.
That’s because despite their mechanical sophistication, most are still surprisingly dumb. They can carry out precision welding on a car or rapidly assemble electronics, but only by rigidly following a prescribed set of motions. Moving cardboard boxes around a warehouse might seem simple to a human, but it actually involves a variety of tasks machines still find pretty difficult—perceiving your surroundings, navigating, and interacting with objects in a dynamic environment.
But the release of this video suggests Boston Dynamics thinks these kinds of applications are close to prime time. Last week the company doubled down by announcing the acquisition of start-up Kinema Systems, which builds computer vision systems for robots working in warehouses.
It’s not the only company making strides in this area. On the same day the video went live, Google unveiled a robot arm called TossingBot that can pick random objects from a box and quickly toss them into another container beyond its reach, which could prove very useful for sorting items in a warehouse. The machine can train on new objects in just an hour or two, and can pick and toss up to 500 items an hour with better accuracy than any of the humans who tried the task.
And an apple-picking robot built by Abundant Robotics is currently on New Zealand farms navigating between rows of apple trees using LIDAR and computer vision to single out ripe apples before using a vacuum tube to suck them off the tree.
In most cases, advances in machine learning and computer vision brought about by the recent AI boom are the keys to these rapidly improving capabilities. Robots have historically had to be painstakingly programmed by humans to solve each new task, but deep learning is making it possible for them to quickly train themselves on a variety of perception, navigation, and dexterity tasks.
It’s not been simple, though, and the application of deep learning in robotics has lagged behind other areas. A major limitation is that the process typically requires huge amounts of training data. That’s fine when you’re dealing with image classification, but when that data needs to be generated by real-world robots it can make the approach impractical. Simulations offer the possibility to run this training faster than real time, but it’s proved difficult to translate policies learned in virtual environments into the real world.
Recent years have seen significant progress on these fronts, though, and the increasing integration of modern machine learning with robotics. In October, OpenAI imbued a robotic hand with human-level dexterity by training an algorithm in a simulation using reinforcement learning before transferring it to the real-world device. The key to ensuring the translation went smoothly was injecting random noise into the simulation to mimic some of the unpredictability of the real world.
And just a couple of weeks ago, MIT researchers demonstrated a new technique that let a robot arm learn to manipulate new objects with far less training data than is usually required. By getting the algorithm to focus on a few key points on the object necessary for picking it up, the system could learn to pick up a previously unseen object after seeing only a few dozen examples (rather than the hundreds or thousands typically required).
How quickly these innovations will trickle down to practical applications remains to be seen, but a number of startups as well as logistics behemoth Amazon are developing robots designed to flexibly pick and place the wide variety of items found in your average warehouse.
Whether the economics of using robots to replace humans at these kinds of menial tasks makes sense yet is still unclear. The collapse of collaborative robotics pioneer Rethink Robotics last year suggests there are still plenty of challenges.
But at the same time, the number of robotic warehouses is expected to leap from 4,000 today to 50,000 by 2025. It may not be long until robots are muscling in on tasks we’ve long assumed only humans could do.
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