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Archivo del Autor: Belen De Leon

Robots learn to grab and scramble with new levels of agility

Robots are amazing things, but outside of their specific domains they are incredibly limited. So flexibility — not physical, but mental — is a constant area of research. A trio of new robotic setups demonstrate ways they can evolve to accommodate novel situations: using both “hands,” getting up after a fall, and understanding visual instructions they’ve never seen before.

The robots, all developed independently, are gathered together today in a special issue of the journal Science Robotics dedicated to learning. Each shows an interesting new way in which robots can improve their interactions with the real world.

On the other hand…

First there is the question of using the right tool for a job. As humans with multi-purpose grippers on the ends of our arms, we’re pretty experienced with this. We understand from a lifetime of touching stuff that we need to use this grip to pick this up, we need to use tools for that, this will be light, that heavy, and so on.

Robots, of course, have no inherent knowledge of this, which can make things difficult; it may not understand that it can’t pick up something of a given size, shape, or texture. A new system from Berkeley roboticists acts as a rudimentary decision-making process, classifying objects as able to be grabbed either by an ordinary pincer grip or with a suction cup grip.

A robot, wielding both simultaneously, decides on the fly (using depth-based imagery) what items to grab and with which tool; the result is extremely high reliability even on piles of objects it’s never seen before.

It’s done with a neural network that consumed millions of data points on items, arrangements, and attempts to grab them. If you attempted to pick up a teddy bear with a suction cup and it didn’t work the first ten thousand times, would you keep on trying? This system learned to make that kind of determination, and as you can imagine such a thing is potentially very important for tasks like warehouse picking for which robots are being groomed.

Interestingly, because of the “black box” nature of complex neural networks, it’s difficult to tell what exactly Dex-Net 4.0 is actually basing its choices on, although there are some obvious preferences, explained Berkeley’s  Ken Goldberg in an email.

“We can try to infer some intuition but the two networks are inscrutable in that we can’t extract understandable ‘policies,’ ” he wrote. “We empirically find that smooth planar surfaces away from edges generally score well on the suction model and pairs of antipodal points generally score well for the gripper.”

Now that reliability and versatility are high, the next step is speed; Goldberg said that the team is “working on an exciting new approach” to reduce computation time for the network, to be documented, no doubt, in a future paper.

ANYmal’s new tricks

Quadrupedal robots are already flexible in that they can handle all kinds of terrain confidently, even recovering from slips (and of course cruel kicks). But when they fall, they fall hard. And generally speaking they don’t get up.

The way these robots have their legs configured makes it difficult to do things in anything other than an upright position. But ANYmal, a robot developed by ETH Zurich (and which you may recall from its little trip to the sewer recently), has a more versatile setup that gives its legs extra degrees of freedom.

What could you do with that extra movement? All kinds of things. But it’s incredibly difficult to figure out the exact best way for the robot to move in order to maximize speed or stability. So why not use a simulation to test thousands of ANYmals trying different things at once, and use the results from that in the real world?

This simulation-based learning doesn’t always work, because it isn’t possible right now to accurately simulate all the physics involved. But it can produce extremely novel behaviors or streamline ones humans thought were already optimal.

At any rate that’s what the researchers did here, and not only did they arrive at a faster trot for the bot (above), but taught it an amazing new trick: getting up from a fall. Any fall. Watch this:

It’s extraordinary that the robot has come up with essentially a single technique to get on its feet from nearly any likely fall position, as long as it has room and the use of all its legs. Remember, people didn’t design this — the simulation and evolutionary algorithms came up with it by trying thousands of different behaviors over and over and keeping the ones that worked.

Ikea assembly is the killer app

Let’s say you were given three bowls, with red and green balls in the center one. Then you’re given this on a sheet of paper:

As a human with a brain, you take this paper for instructions, and you understand that the green and red circles represent balls of those colors, and that red ones need to go to the left, while green ones go to the right.

This is one of those things where humans apply vast amounts of knowledge and intuitive understanding without even realizing it. How did you choose to decide the circles represent the balls? Because of the shape? Then why don’t the arrows refer to “real” arrows? How do you know how far to go to the right or left? How do you know the paper even refers to these items at all? All questions you would resolve in a fraction of a second, and any of which might stump a robot.

Researchers have taken some baby steps towards being able to connect abstract representations like the above with the real world, a task that involves a significant amount of what amounts to a sort of machine creativity or imagination.

Making the connection between a green dot on a white background in a diagram and a greenish roundish thing on a black background in the real world isn’t obvious, but the “visual cognitive computer” created by Miguel Lázaro-Gredilla and his colleagues at Vicarious AI seems to be doing pretty well at it.

It’s still very primitive, of course, but in theory it’s the same toolset that one uses to, for example, assemble a piece of Ikea furniture: look at an abstract representation, connect it to real-world objects, then manipulate those objects according to the instructions. We’re years away from that, but it wasn’t long ago that we were years away from a robot getting up from a fall or deciding a suction cup or pincer would work better to pick something up.

The papers and videos demonstrating all the concepts above should be available at the Science Robotics site.


Source: TechCrunch

Anti-Trump Activists Defend Fake-*Washington Post* Stunt

Protesters have created satirical newspapers before, but the tactic comes with more baggage in the era of fake news.
Source: Wired

A Crocodile-Like Robot Helps Solve a 300-Million-Year Mystery

Researchers use a fossil, fancy computer work, and a complex robot to tease apart how an early land-walking animal moved.
Source: Wired

Hackers are scoring with ransomware that attacks its previous victims

Computer viruses are always evolving. In a new one, dubbed “Ryuk,” hackers are targeting PCs with ransomware that scours an infected network in order to pinpoint and attack and enterprises with big money.

The post Hackers are scoring with ransomware that attacks its previous victims appeared first on Digital Trends.


Source: Digital trends

CES 2019 recap: All the trends, products, and gadgets you missed

CES 2019 didn’t just give us a taste of the future, it offered a five-course meal. From 8K and Micro LED televisions to smart toilets, the show delivered with all the amazing gadgetry you could ask for. Here’s a look at all the big trends, and when expect they’ll arrive.

The post CES 2019 recap: All the trends, products, and gadgets you missed appeared first on Digital Trends.


Source: Digital trends

Peloton Tread review: Makes running at home much more fun for rich people – CNET

I don’t like running, but the Peloton Tread was surprisingly fun to use.
Source: CNET

My favorite AirPod-alternative wire-free earbuds are now just $33 – CNET

After several months of using these (and trying lots of others), I continue to enthusiastically recommend them. Plus: Don’t mind wires? These sport earbuds are only $17.
Source: CNET

AirPower reference appears in Apple's iPhone XS battery case listing – CNET

A mention of Apple’s long-awaited wireless charging pad surfaced in the company’s online store early Wednesday.
Source: CNET

Hubble Space Telescope may hang in there through 2025 – CNET

The assessment comes after NASA tried to bring its main camera back online.
Source: CNET

Infor lands $1.5 billion investment ahead of possible IPO

Infor, a NYC-based enterprise software company, announced a massive $1.5 billion investment today that could be the precursor to an IPO down the road. One analyst is estimating that the valuation could be at least $60 billion.

The investment is being led by Koch Industries’ investment arm, Koch Equity Development, and Golden Gate Capital. Today’s investment comes on top of a $2 billion+ cash infusion from Koch in 2017, bringing the total raised to at least more than $3.5 billion. That’s a lot of cash.

Infor may be the largest company you never heard of with over 17,000 employees and over 9500 customers in more than 100 countries worldwide. All of those customers generated $3 billion in revenue in 2018. That’s a significant presence.

Ray Wang, founder and principal analyst at Constellation Research, told TechCrunch that based on that revenue, he believes the valuation could be in the neighborhood of $60 billion. He based that on $3 billion in revenue, while using Oracle and SAP as similar industry comparisons. These companies have 20X price/earnings ratio. He adds, that would make it the largest tech IPO ever for NYC tech company if that comes to pass. Infor would not confirm this number with a spokesperson telling TechCrunch, “We cannot comment on value at this time.”

What does this company do to achieve this size and scope? It’s not unlike many other large enterprise companies, says Wang. It produces cloud software solutions around typical enterprise needs such CRM, ERP and Supply Chain Asset Management.

Daniel Newman, principal analyst at Futurum Research says that Infor, has grown rapidly through a series of acquisitions and an unusual approach to enterprise software. “What makes its approach to enterprise software unique is that rather than building software and then attempting to customize it for the unique [customer] needs, Infor takes an industry-based approach that incorporates both subtle and material capabilities to address specific industry needs that more generic ERP tools aren’t capable of out of the box,” Newman told TechCrunch.

He adds that this difference is attractive to many companies seeking ERP and Enterprise Asset Management tools that are built with their business in mind rather than completely customizing a software designed for any business in any industry.

As it turns out, Koch isn’t just an investor, it’s an Infor customer. “Koch was a customer of Infor before we became an investor in the company, and Koch Industries’ companies continue to move their most mission critical applications to Infor CloudSuites,” Jim Hannan, Executive Vice President and CEO for Enterprises at Koch Industries said in a statement.

The company, which was founded way back in 2002, has been shifting to the cloud over the last five years. It reports that over 70 percent of its revenue is now derived from cloud products, fueled in part by an aggressive acquisition strategy.

Note: There is an investor call shortly. We will update the story with any pertinent information after the call.


Source: TechCrunch

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