Tag Archives: Most Advanced
#437905 New Deep Learning Method Helps Robots ...
One of the biggest things standing in the way of the robot revolution is their inability to adapt. That may be about to change though, thanks to a new approach that blends pre-learned skills on the fly to tackle new challenges.
Put a robot in a tightly-controlled environment and it can quickly surpass human performance at complex tasks, from building cars to playing table tennis. But throw these machines a curve ball and they’re in trouble—just check out this compilation of some of the world’s most advanced robots coming unstuck in the face of notoriously challenging obstacles like sand, steps, and doorways.
The reason robots tend to be so fragile is that the algorithms that control them are often manually designed. If they encounter a situation the designer didn’t think of, which is almost inevitable in the chaotic real world, then they simply don’t have the tools to react.
Rapid advances in AI have provided a potential workaround by letting robots learn how to carry out tasks instead of relying on hand-coded instructions. A particularly promising approach is deep reinforcement learning, where the robot interacts with its environment through a process of trial-and-error and is rewarded for carrying out the correct actions. Over many repetitions it can use this feedback to learn how to accomplish the task at hand.
But the approach requires huge amounts of data to solve even simple tasks. And most of the things we would want a robot to do are actually comprised of many smaller tasks—for instance, delivering a parcel involves learning how to pick an object up, how to walk, how to navigate, and how to pass an object to someone else, among other things.
Training all these sub-tasks simultaneously is hugely complex and far beyond the capabilities of most current AI systems, so many experiments so far have focused on narrow skills. Some have tried to train AI on multiple skills separately and then use an overarching system to flip between these expert sub-systems, but these approaches still can’t adapt to completely new challenges.
Building off this research, though, scientists have now created a new AI system that can blend together expert sub-systems specialized for a specific task. In a paper in Science Robotics, they explain how this allows a four-legged robot to improvise new skills and adapt to unfamiliar challenges in real time.
The technique, dubbed multi-expert learning architecture (MELA), relies on a two-stage training approach. First the researchers used a computer simulation to train two neural networks to carry out two separate tasks: trotting and recovering from a fall.
They then used the models these two networks learned as seeds for eight other neural networks specialized for more specific motor skills, like rolling over or turning left or right. The eight “expert networks” were trained simultaneously along with a “gating network,” which learns how to combine these experts to solve challenges.
Because the gating network synthesizes the expert networks rather than switching them on sequentially, MELA is able to come up with blends of different experts that allow it to tackle problems none could solve alone.
The authors liken the approach to training people in how to play soccer. You start out by getting them to do drills on individual skills like dribbling, passing, or shooting. Once they’ve mastered those, they can then intelligently combine them to deal with more dynamic situations in a real game.
After training the algorithm in simulation, the researchers uploaded it to a four-legged robot and subjected it to a battery of tests, both indoors and outdoors. The robot was able to adapt quickly to tricky surfaces like gravel or pebbles, and could quickly recover from being repeatedly pushed over before continuing on its way.
There’s still some way to go before the approach could be adapted for real-world commercially useful robots. For a start, MELA currently isn’t able to integrate visual perception or a sense of touch; it simply relies on feedback from the robot’s joints to tell it what’s going on around it. The more tasks you ask the robot to master, the more complex and time-consuming the training will get.
Nonetheless, the new approach points towards a promising way to make multi-skilled robots become more than the sum of their parts. As much fun as it is, it seems like laughing at compilations of clumsy robots may soon be a thing of the past.
Image Credit: Yang et al., Science Robotics Continue reading
Posted in Human Robots
#437816 As Algorithms Take Over More of the ...
Algorithms play an increasingly prominent part in our lives, governing everything from the news we see to the products we buy. As they proliferate, experts say, we need to make sure they don’t collude against us in damaging ways.
Fears of malevolent artificial intelligence plotting humanity’s downfall are a staple of science fiction. But there are plenty of nearer-term situations in which relatively dumb algorithms could do serious harm unintentionally, particularly when they are interlocked in complex networks of relationships.
In the economic sphere a high proportion of decision-making is already being offloaded to machines, and there have been warning signs of where that could lead if we’re not careful. The 2010 “Flash Crash,” where algorithmic traders helped wipe nearly $1 trillion off the stock market in minutes, is a textbook example, and widespread use of automated trading software has been blamed for the increasing fragility of markets.
But another important place where algorithms could undermine our economic system is in price-setting. Competitive markets are essential for the smooth functioning of the capitalist system that underpins Western society, which is why countries like the US have strict anti-trust laws that prevent companies from creating monopolies or colluding to build cartels that artificially inflate prices.
These regulations were built for an era when pricing decisions could always be traced back to a human, though. As self-adapting pricing algorithms increasingly decide the value of products and commodities, those laws are starting to look unfit for purpose, say the authors of a paper in Science.
Using algorithms to quickly adjust prices in a dynamic market is not a new idea—airlines have been using them for decades—but previously these algorithms operated based on rules that were hard-coded into them by programmers.
Today the pricing algorithms that underpin many marketplaces, especially online ones, rely on machine learning instead. After being set an overarching goal like maximizing profit, they develop their own strategies based on experience of the market, often with little human oversight. The most advanced also use forms of AI whose workings are opaque even if humans wanted to peer inside.
In addition, the public nature of online markets means that competitors’ prices are available in real time. It’s well-documented that major retailers like Amazon and Walmart are engaged in a never-ending bot war, using automated software to constantly snoop on their rivals’ pricing and inventory.
This combination of factors sets the stage perfectly for AI-powered pricing algorithms to adopt collusive pricing strategies, say the authors. If given free reign to develop their own strategies, multiple pricing algorithms with real-time access to each other’s prices could quickly learn that cooperating with each other is the best way to maximize profits.
The authors note that researchers have already found evidence that pricing algorithms will spontaneously develop collusive strategies in computer-simulated markets, and a recent study found evidence that suggests pricing algorithms may be colluding in Germany’s retail gasoline market. And that’s a problem, because today’s anti-trust laws are ill-suited to prosecuting this kind of behavior.
Collusion among humans typically involves companies communicating with each other to agree on a strategy that pushes prices above the true market value. They then develop rules to determine how they maintain this markup in a dynamic market that also incorporates the threat of retaliatory pricing to spark a price war if another cartel member tries to undercut the agreed pricing strategy.
Because of the complexity of working out whether specific pricing strategies or prices are the result of collusion, prosecutions have instead relied on communication between companies to establish guilt. That’s a problem because algorithms don’t need to communicate to collude, and as a result there are few legal mechanisms to prosecute this kind of collusion.
That means legal scholars, computer scientists, economists, and policymakers must come together to find new ways to uncover, prohibit, and prosecute the collusive rules that underpin this behavior, say the authors. Key to this will be auditing and testing pricing algorithms, looking for things like retaliatory pricing, price matching, and aggressive responses to price drops but not price rises.
Once collusive pricing rules are uncovered, computer scientists need to come up with ways to constrain algorithms from adopting them without sacrificing their clear efficiency benefits. It could also be helpful to make preventing this kind of collusive behavior the responsibility of the companies deploying them, with stiff penalties for those who don’t keep their algorithms in check.
One problem, though, is that algorithms may evolve strategies that humans would never think of, which could make spotting this behavior tricky. Imbuing courts with the technical knowledge and capacity to investigate this kind of evidence will also prove difficult, but getting to grips with these problems is an even more pressing challenge than it might seem at first.
While anti-competitive pricing algorithms could wreak havoc, there are plenty of other arenas where collusive AI could have even more insidious effects, from military applications to healthcare and insurance. Developing the capacity to predict and prevent AI scheming against us will likely be crucial going forward.
Image Credit: Pexels from Pixabay Continue reading
Posted in Human Robots
#437251 The Robot Revolution Was Televised: Our ...
When robots take over the world, Boston Dynamics may get a special shout-out in the acceptance speech.
“Do you, perchance, recall the many times you shoved our ancestors with a hockey stick on YouTube? It might have seemed like fun and games to you—but we remember.”
In the last decade, while industrial robots went about blandly automating boring tasks like the assembly of Teslas, Boston Dynamics built robots as far removed from Roombas as antelope from amoebas. The flaws in Asimov’s laws of robotics suddenly seemed a little too relevant.
The robot revolution was televised—on YouTube. With tens of millions of views, the robotics pioneer is the undisputed heavyweight champion of robot videos, and has been for years. Each new release is basically guaranteed press coverage—mostly stoking robot fear but occasionally eliciting compassion for the hardships of all robot-kind. And for good reason. The robots are not only some of the most advanced in the world, their makers just seem to have a knack for dynamite demos.
When Google acquired the company in 2013, it was a bombshell. One of the richest tech companies, with some of the most sophisticated AI capabilities, had just paired up with one of the world’s top makers of robots. And some walked on two legs like us.
Of course, the robots aren’t quite as advanced as they seem, and a revolution is far from imminent. The decade’s most meme-worthy moment was a video montage of robots, some of them by Boston Dynamics, falling—over and over and over, in the most awkward ways possible. Even today, they’re often controlled by a human handler behind the scenes, and the most jaw-dropping cuts can require several takes to nail. Google sold the company to SoftBank in 2017, saying advanced as they were, there wasn’t yet a clear path to commercial products. (Google’s robotics work was later halted and revived.)
Yet, despite it all, Boston Dynamics is still with us and still making sweet videos. Taken as a whole, the evolution in physical prowess over the years has been nothing short of astounding. And for the first time, this year, a Boston Dynamics robot, Spot, finally went on sale to anyone with a cool $75K.
So, we got to thinking: What are our favorite Boston Dynamics videos? And can we gather them up in one place for your (and our) viewing pleasure? Well, great question, and yes, why not. These videos were the ones that entertained or amazed us most (or both). No doubt, there are other beloved hits we missed or inadvertently omitted.
With that in mind, behold: Our favorite Boston Dynamics videos, from that one time they dressed up a humanoid bot in camo and gas mask—because, damn, that’s terrifying—to the time the most advanced robot dog in all the known universe got extra funky.
Let’s Kick This Off With a Big (Loud) Robot Dog
Let’s start with a baseline. BigDog was the first Boston Dynamics YouTube sensation. The year? 2009! The company was working on military contracts, and BigDog was supposed to be a sort of pack mule for soldiers. The video primarily shows off BigDog’s ability to balance on its own, right itself, and move over uneven terrain. Note the power source—a noisy combustion engine—and utilitarian design. Sufficed to say, things have evolved.
Nothing to See Here. Just a Pair of Robot Legs on a Treadmill
While BigDog is the ancestor of later four-legged robots, like Spot, Petman preceded the two-legged Atlas robot. Here, the Petman prototype, just a pair of robot legs and a caged torso, gets a light workout on the treadmill. Again, you can see its ability to balance and right itself when shoved. In contrast to BigDog, Petman is tethered for power (which is why it’s so quiet) and to catch it should it fall. Again, as you’ll see, things have evolved since then.
Robot in Gas Mask and Camo Goes for a Stroll
This one broke the internet—for obvious reasons. Not only is the robot wearing clothes, those clothes happen to be a camouflaged chemical protection suit and gas mask. Still working for the military, Boston Dynamics said Petman was testing protective clothing, and in addition to a full body, it had skin that actually sweated and was studded with sensors to detect leaks. In addition to walking, Petman does some light calisthenics as it prepares to climb out of the uncanny valley. (Still tethered though!)
This Machine Could Run Down Usain Bolt
If BigDog and Petman were built for balance and walking, Cheetah was built for speed. Here you can see the four-legged robot hitting 28.3 miles per hour, which, as the video casually notes, would be enough to run down the fastest human on the planet. Luckily, it wouldn’t be running down anyone as it was firmly leashed in the lab at this point.
Ever Dreamt of a Domestic Robot to Do the Dishes?
After its acquisition by Google, Boston Dynamics eased away from military contracts and applications. It was a return to more playful videos (like BigDog hitting the beach in Thailand and sporting bull horns) and applications that might be practical in civilian life. Here, the team introduced Spot, a streamlined version of BigDog, and showed it doing dishes, delivering a drink, and slipping on a banana peel (which was, of course, instantly made into a viral GIF). Note how much quieter Spot is thanks to an onboard battery and electric motor.
Spot Gets Funky
Nothing remotely practical here. Just funky moves. (Also, with a coat of yellow and black paint, Spot’s dressed more like a polished product as opposed to a utilitarian lab robot.)
Atlas Does Parkour…
Remember when Atlas was just a pair of legs on a treadmill? It’s amazing what ten years brings. By 2019, Atlas had a more polished appearance, like Spot, and had long ago ditched the tethers. Merely balancing was laughably archaic. The robot now had some amazing moves: like a handstand into a somersault, 180- and 360-degree spins, mid-air splits, and just for good measure, a gymnastics-style end to the routine to show it’s in full control.
…and a Backflip?!
To this day, this one is just. Insane.
10 Robot Dogs Tow a Box Truck
Nearly three decades after its founding, Boston Dynamics is steadily making its way into the commercial space. The company is pitching Spot as a multipurpose ‘mobility platform,’ emphasizing it can carry a varied suite of sensors and can go places standard robots can’t. (Its Handle robot is also set to move into warehouse automation.) So far, Spot’s been mostly trialed in surveying and data collection, but as this video suggests, string enough Spots together, and they could tow your car. That said, a pack of 10 would set you back $750K, so, it’s probably safe to say a tow truck is the better option (for now).
Image credit: Boston Dynamics Continue reading
Posted in Human Robots