Tag Archives: manipulation

#433506 MIT’s New Robot Taught Itself to Pick ...

Posted on September 18, 2018 by Android

Back in 2016, somewhere in a Google-owned warehouse, more than a dozen robotic arms sat for hours quietly grasping objects of various shapes and sizes. For hours on end, they taught themselves how to pick up and hold the items appropriately—mimicking the way a baby gradually learns to use its hands.

Now, scientists from MIT have made a new breakthrough in machine learning: their new system can not only teach itself to see and identify objects, but also understand how best to manipulate them.

This means that, armed with the new machine learning routine referred to as “dense object nets (DON),” the robot would be capable of picking up an object that it’s never seen before, or in an unfamiliar orientation, without resorting to trial and error—exactly as a human would.

The deceptively simple ability to dexterously manipulate objects with our hands is a huge part of why humans are the dominant species on the planet. We take it for granted. Hardware innovations like the Shadow Dexterous Hand have enabled robots to softly grip and manipulate delicate objects for many years, but the software required to control these precision-engineered machines in a range of circumstances has proved harder to develop.

This was not for want of trying. The Amazon Robotics Challenge offers millions of dollars in prizes (and potentially far more in contracts, as their $775m acquisition of Kiva Systems shows) for the best dexterous robot able to pick and package items in their warehouses. The lucrative dream of a fully-automated delivery system is missing this crucial ability.

Meanwhile, the Robocup@home challenge—an offshoot of the popular Robocup tournament for soccer-playing robots—aims to make everyone’s dream of having a robot butler a reality. The competition involves teams drilling their robots through simple household tasks that require social interaction or object manipulation, like helping to carry the shopping, sorting items onto a shelf, or guiding tourists around a museum.

Yet all of these endeavors have proved difficult; the tasks often have to be simplified to enable the robot to complete them at all. New or unexpected elements, such as those encountered in real life, more often than not throw the system entirely. Programming the robot’s every move in explicit detail is not a scalable solution: this can work in the highly-controlled world of the assembly line, but not in everyday life.

Computer vision is improving all the time. Neural networks, including those you train every time you prove that you’re not a robot with CAPTCHA, are getting better at sorting objects into categories, and identifying them based on sparse or incomplete data, such as when they are occluded, or in different lighting.

But many of these systems require enormous amounts of input data, which is impractical, slow to generate, and often needs to be laboriously categorized by humans. There are entirely new jobs that require people to label, categorize, and sift large bodies of data ready for supervised machine learning. This can make machine learning undemocratic. If you’re Google, you can make thousands of unwitting volunteers label your images for you with CAPTCHA. If you’re IBM, you can hire people to manually label that data. If you’re an individual or startup trying something new, however, you will struggle to access the vast troves of labeled data available to the bigger players.

This is why new systems that can potentially train themselves over time or that allow robots to deal with situations they’ve never seen before without mountains of labelled data are a holy grail in artificial intelligence. The work done by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is part of a new wave of “self-supervised” machine learning systems—little of the data used was labeled by humans.

The robot first inspects the new object from multiple angles, building up a 3D picture of the object with its own coordinate system. This then allows the robotic arm to identify a particular feature on the object—such as a handle, or the tongue of a shoe—from various different angles, based on its relative distance to other grid points.

This is the real innovation: the new means of representing objects to grasp as mapped-out 3D objects, with grid points and subsections of their own. Rather than using a computer vision algorithm to identify a door handle, and then activating a door handle grasping subroutine, the DON system treats all objects by making these spatial maps before classifying or manipulating them, enabling it to deal with a greater range of objects than in other approaches.

“Many approaches to manipulation can’t identify specific parts of an object across the many orientations that object may encounter,” said PhD student Lucas Manuelli, who wrote a new paper about the system with lead author and fellow student Pete Florence, alongside MIT professor Russ Tedrake. “For example, existing algorithms would be unable to grasp a mug by its handle, especially if the mug could be in multiple orientations, like upright, or on its side.”

Class-specific descriptors, which can be applied to the object features, can allow the robot arm to identify a mug, find the handle, and pick the mug up appropriately. Object-specific descriptors allow the robot arm to select a particular mug from a group of similar items. I’m already dreaming of a robot butler reliably picking my favourite mug when it serves me coffee in the morning.

Google’s robot arm-y was an attempt to develop a general grasping algorithm: one that could identify, categorize, and appropriately grip as many items as possible. This requires a great deal of training time and data, which is why Google parallelized their project by having 14 robot arms feed data into a single neural network brain: even then, the algorithm may fail with highly specific tasks. Specialist grasping algorithms might require less training if they’re limited to specific objects, but then your software is useless for general tasks.

As the roboticists noted, their system, with its ability to identify parts of an object rather than just a single object, is better suited to specific tasks, such as “grasp the racquet by the handle,” than Amazon Robotics Challenge robots, which identify whole objects by segmenting an image.

This work is small-scale at present. It has been tested with a few classes of objects, including shoes, hats, and mugs. Yet the use of these dense object nets as a way for robots to represent and manipulate new objects may well be another step towards the ultimate goal of generalized automation: a robot capable of performing every task a person can. If that point is reached, the question that will remain is how to cope with being obsolete.

Image Credit: Tom Buehler/CSAIL Continue reading →

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#433307 Toyota AI Ventures Wants to Fund Your ...

Posted on September 10, 2018 by Android

Early stage robotics startups can get up to $2 million in funding from Toyota to work on home assistive robots Continue reading →

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#433288 The New AI Tech Turning Heads in Video ...

Posted on September 9, 2018 by Android

A new technique using artificial intelligence to manipulate video content gives new meaning to the expression “talking head.”

An international team of researchers showcased the latest advancement in synthesizing facial expressions—including mouth, eyes, eyebrows, and even head position—in video at this month’s 2018 SIGGRAPH, a conference on innovations in computer graphics, animation, virtual reality, and other forms of digital wizardry.

The project is called Deep Video Portraits. It relies on a type of AI called generative adversarial networks (GANs) to modify a “target” actor based on the facial and head movement of a “source” actor. As the name implies, GANs pit two opposing neural networks against one another to create a realistic talking head, right down to the sneer or raised eyebrow.

In this case, the adversaries are actually working together: One neural network generates content, while the other rejects or approves each effort. The back-and-forth interplay between the two eventually produces a realistic result that can easily fool the human eye, including reproducing a static scene behind the head as it bobs back and forth.

The researchers say the technique can be used by the film industry for a variety of purposes, from editing facial expressions of actors for matching dubbed voices to repositioning an actor’s head in post-production. AI can not only produce highly realistic results, but much quicker ones compared to the manual processes used today, according to the researchers. You can read the full paper of their work here.

“Deep Video Portraits shows how such a visual effect could be created with less effort in the future,” said Christian Richardt, from the University of Bath’s motion capture research center CAMERA, in a press release. “With our approach, even the positioning of an actor’s head and their facial expression could be easily edited to change camera angles or subtly change the framing of a scene to tell the story better.”

AI Tech Different Than So-Called “Deepfakes”
The work is far from the first to employ AI to manipulate video and audio. At last year’s SIGGRAPH conference, researchers from the University of Washington showcased their work using algorithms that inserted audio recordings from a person in one instance into a separate video of the same person in a different context.

In this case, they “faked” a video using a speech from former President Barack Obama addressing a mass shooting incident during his presidency. The AI-doctored video injects the audio into an unrelated video of the president while also blending the facial and mouth movements, creating a pretty credible job of lip synching.

A previous paper by many of the same scientists on the Deep Video Portraits project detailed how they were first able to manipulate a video in real time of a talking head (in this case, actor and former California governor Arnold Schwarzenegger). The Face2Face system pulled off this bit of digital trickery using a depth-sensing camera that tracked the facial expressions of an Asian female source actor.

A less sophisticated method of swapping faces using a machine learning software dubbed FakeApp emerged earlier this year. Predictably, the tech—requiring numerous photos of the source actor in order to train the neural network—was used for more juvenile pursuits, such as injecting a person’s face onto a porn star.

The application gave rise to the term “deepfakes,” which is now used somewhat ubiquitously to describe all such instances of AI-manipulated video—much to the chagrin of some of the researchers involved in more legitimate uses.

Fighting AI-Created Video Forgeries
However, the researchers are keenly aware that their work—intended for benign uses such as in the film industry or even to correct gaze and head positions for more natural interactions through video teleconferencing—could be used for nefarious purposes. Fake news is the most obvious concern.

“With ever-improving video editing technology, we must also start being more critical about the video content we consume every day, especially if there is no proof of origin,” said Michael Zollhöfer, a visiting assistant professor at Stanford University and member of the Deep Video Portraits team, in the press release.

Toward that end, the research team is training the same adversarial neural networks to spot video forgeries. They also strongly recommend that developers clearly watermark videos that are edited through AI or otherwise, and denote clearly what part and element of the scene was modified.

To catch less ethical users, the US Department of Defense, through the Defense Advanced Research Projects Agency (DARPA), is supporting a program called Media Forensics. This latest DARPA challenge enlists researchers to develop technologies to automatically assess the integrity of an image or video, as part of an end-to-end media forensics platform.

The DARPA official in charge of the program, Matthew Turek, did tell MIT Technology Review that so far the program has “discovered subtle cues in current GAN-manipulated images and videos that allow us to detect the presence of alterations.” In one reported example, researchers have targeted eyes, which rarely blink in the case of “deepfakes” like those created by FakeApp, because the AI is trained on still pictures. That method would seem to be less effective to spot the sort of forgeries created by Deep Video Portraits, which appears to flawlessly match the entire facial and head movements between the source and target actors.

“We believe that the field of digital forensics should and will receive a lot more attention in the future to develop approaches that can automatically prove the authenticity of a video clip,” Zollhöfer said. “This will lead to ever-better approaches that can spot such modifications even if we humans might not be able to spot them with our own eyes.

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#433278 Outdated Evolution: Updating Our ...

Posted on September 9, 2018 by Android

What happens when evolution shapes an animal for tribes of 150 primitive individuals living in a chaotic jungle, and then suddenly that animal finds itself living with millions of others in an engineered metropolis, their pockets all bulging with devices of godlike power?

The result, it seems, is a modern era of tension where archaic forms of governance struggle to keep up with the technological advances of their citizenry, where governmental policies act like constraining bottlenecks rather than spearheads of progress.

Simply put, our governments have failed to adapt to disruptive technologies. And if we are to regain our stability moving forward into a future of even greater disruption, it’s imperative that we understand the issues that got us into this situation and what kind of solutions we can engineer to overcome our governmental weaknesses.

Hierarchy vs. Technological Decentralization
Many of the greatest issues our governments face today come from humanity’s biologically-hardwired desire for centralized hierarchies. This innate proclivity towards building and navigating systems of status and rank were evolutionary gifts handed down to us by our ape ancestors, where each member of a community had a mental map of their social hierarchy. Their nervous systems behaved differently depending on their rank in this hierarchy, influencing their interactions in a way that ensured only the most competent ape would rise to the top to gain access to the best food and mates.

As humanity emerged and discovered the power of language, we continued this practice by ensuring that those at the top of the hierarchies, those with the greatest education and access to information, were the dominant decision-makers for our communities.

However, this kind of structured chain of power is only necessary if we’re operating in conditions of scarcity. But resources, including information, are no longer scarce.

It’s estimated that more than two-thirds of adults in the world now own a smartphone, giving the average citizen the same access to the world’s information as the leaders of our governments. And with global poverty falling from 35.5 percent to 10.9 percent over the last 25 years, our younger generations are growing up seeing automation and abundance as a likely default, where innovations like solar energy, lab-grown meat, and 3D printing are expected to become commonplace.

It’s awareness of this paradigm shift that has empowered the recent rise of decentralization. As information and access to resources become ubiquitous, there is noticeably less need for our inefficient and bureaucratic hierarchies.

For example, if blockchain can prove its feasibility for large-scale systems, it can be used to update and upgrade numerous applications to a decentralized model, including currency and voting. Such innovations would lower the risk of failing banks collapsing the economy like they did in 2008, as well as prevent corrupt politicians from using gerrymandering and long queues at polling stations to deter voter participation.

Of course, technology isn’t a magic wand that should be implemented carelessly. Facebook’s “move fast and break things” approach might have very possibly broken American democracy in 2016, as social media played on some of the worst tendencies humanity can operate on during an election: fear and hostility.

But if decentralized technology, like blockchain’s public ledgers, can continue to spread a sense of security and transparency throughout society, perhaps we can begin to quiet that paranoia and hyper-vigilance our brains evolved to cope with living as apes in dangerous jungles. By decentralizing our power structures, we take away the channels our outdated biological behaviors might use to enact social dominance and manipulation.

The peace of mind this creates helps to reestablish trust in our communities and in our governments. And with trust in the government increased, it’s likely we’ll see our next issue corrected.

From Business and Law to Science and Technology
A study found that 59 percent of US presidents, 68 percent of vice presidents, and 78 percent of secretaries of state were lawyers by education and occupation. That’s more than one out of every two people in the most powerful positions in the American government restricted to a field dedicated to convincing other people (judges) their perspective is true, even if they lack evidence.

And so the scientific method became less important than semantics to our leaders.

Similarly, of the 535 individuals in the American congress, only 24 hold a PhD, only 2 of which are in a STEM field. And so far, it’s not getting better: Trump is the first president since WWII not to name a science advisor.

But if we can use technologies like blockchain to increase transparency, efficiency, and trust in the government, then the upcoming generations who understand decentralization, abundance, and exponential technologies might feel inspired enough to run for government positions. This helps solve that common problem where the smartest and most altruistic people tend to avoid government positions because they don’t want to play the semantic and deceitful game of politics.

By changing this narrative, our governments can begin to fill with techno-progressive individuals who actually understand the technologies that are rapidly reshaping our reality. And this influence of expertise is going to be crucial as our governments are forced to restructure and create new policies to accommodate the incoming disruption.

Clearing Regulations to Begin Safe Experimentation
As exponential technologies become more ubiquitous, we’re likely going to see young kids and garage tinkerers creating powerful AIs and altering genetics thanks to tools like CRISPR and free virtual reality tutorials.

This easy accessibility to such powerful technology means unexpected and rapid progress can occur almost overnight, quickly overwhelming our government’s regulatory systems.

Uber and Airbnb are two of the best examples of our government’s inability to keep up with such technology, both companies achieving market dominance before regulators were even able to consider how to handle them. And when a government has decided against them, they often still continue to operate because people simply choose to keep using the apps.

Luckily, this kind of disruption hasn’t yet posed a major existential threat. But this will change when we see companies begin developing cyborg body parts, brain-computer interfaces, nanobot health injectors, and at-home genetic engineering kits.

For this reason, it’s crucial that we have experts who understand how to update our regulations to be as flexible as is necessary to ensure we don’t create black market conditions like we’ve done with drugs. It’s better to have safe and monitored experimentation, rather than forcing individuals into seedy communities using unsafe products.

Survival of the Most Adaptable
If we hope to be an animal that survives our changing environment, we have to adapt. We cannot cling to the behaviors and systems formed thousands of years ago. We must instead acknowledge that we now exist in an ecosystem of disruptive technology, and we must evolve and update our governments if they’re going to be capable of navigating these transformative impacts.

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#432671 Stuff 3.0: The Era of Programmable ...

Posted on April 30, 2018 by Android

It’s the end of a long day in your apartment in the early 2040s. You decide your work is done for the day, stand up from your desk, and yawn. “Time for a film!” you say. The house responds to your cues. The desk splits into hundreds of tiny pieces, which flow behind you and take on shape again as a couch. The computer screen you were working on flows up the wall and expands into a flat projection screen. You relax into the couch and, after a few seconds, a remote control surfaces from one of its arms.

In a few seconds flat, you’ve gone from a neatly-equipped office to a home cinema…all within the same four walls. Who needs more than one room?

This is the dream of those who work on “programmable matter.”

In his recent book about AI, Max Tegmark makes a distinction between three different levels of computational sophistication for organisms. Life 1.0 is single-celled organisms like bacteria; here, hardware is indistinguishable from software. The behavior of the bacteria is encoded into its DNA; it cannot learn new things.

Life 2.0 is where humans live on the spectrum. We are more or less stuck with our hardware, but we can change our software by choosing to learn different things, say, Spanish instead of Italian. Much like managing space on your smartphone, your brain’s hardware will allow you to download only a certain number of packages, but, at least theoretically, you can learn new behaviors without changing your underlying genetic code.

Life 3.0 marks a step-change from this: creatures that can change both their hardware and software in something like a feedback loop. This is what Tegmark views as a true artificial intelligence—one that can learn to change its own base code, leading to an explosion in intelligence. Perhaps, with CRISPR and other gene-editing techniques, we could be using our “software” to doctor our “hardware” before too long.

Programmable matter extends this analogy to the things in our world: what if your sofa could “learn” how to become a writing desk? What if, instead of a Swiss Army knife with dozens of tool attachments, you just had a single tool that “knew” how to become any other tool you could require, on command? In the crowded cities of the future, could houses be replaced by single, OmniRoom apartments? It would save space, and perhaps resources too.

Such are the dreams, anyway.

But when engineering and manufacturing individual gadgets is such a complex process, you can imagine that making stuff that can turn into many different items can be extremely complicated. Professor Skylar Tibbits at MIT referred to it as 4D printing in a TED Talk, and the website for his research group, the Self-Assembly Lab, excitedly claims, “We have also identified the key ingredients for self-assembly as a simple set of responsive building blocks, energy and interactions that can be designed within nearly every material and machining process available. Self-assembly promises to enable breakthroughs across many disciplines, from biology to material science, software, robotics, manufacturing, transportation, infrastructure, construction, the arts, and even space exploration.”

Naturally, their projects are still in the early stages, but the Self-Assembly Lab and others are genuinely exploring just the kind of science fiction applications we mooted.

For example, there’s the cell-phone self-assembly project, which brings to mind eerie, 24/7 factories where mobile phones assemble themselves from 3D printed kits without human or robotic intervention. Okay, so the phones they’re making are hardly going to fly off the shelves as fashion items, but if all you want is something that works, it could cut manufacturing costs substantially and automate even more of the process.

One of the major hurdles to overcome in making programmable matter a reality is choosing the right fundamental building blocks. There’s a very important balance to strike. To create fine details, you need to have things that aren’t too big, so as to keep your rearranged matter from being too lumpy. This might make the building blocks useless for certain applications—for example, if you wanted to make tools for fine manipulation. With big pieces, it might be difficult to simulate a range of textures. On the other hand, if the pieces are too small, different problems can arise.

Imagine a setup where each piece is a small robot. You have to contain the robot’s power source and its brain, or at least some kind of signal-generator and signal-processor, all in the same compact unit. Perhaps you can imagine that one might be able to simulate a range of textures and strengths by changing the strength of the “bond” between individual units—your desk might need to be a little bit more firm than your bed, which might be nicer with a little more give.

Early steps toward creating this kind of matter have been taken by those who are developing modular robots. There are plenty of different groups working on this, including MIT, Lausanne, and the University of Brussels.

In the latter configuration, one individual robot acts as a centralized decision-maker, referred to as the brain unit, but additional robots can autonomously join the brain unit as and when needed to change the shape and structure of the overall system. Although the system is only ten units at present, it’s a proof-of-concept that control can be orchestrated over a modular system of robots; perhaps in the future, smaller versions of the same thing could be the components of Stuff 3.0.

You can imagine that with machine learning algorithms, such swarms of robots might be able to negotiate obstacles and respond to a changing environment more easily than an individual robot (those of you with techno-fear may read “respond to a changing environment” and imagine a robot seamlessly rearranging itself to allow a bullet to pass straight through without harm).

Speaking of robotics, the form of an ideal robot has been a subject of much debate. In fact, one of the major recent robotics competitions—DARPA’s Robotics Challenge—was won by a robot that could adapt, beating Boston Dynamics’ infamous ATLAS humanoid with the simple addition of a wheel that allowed it to drive as well as walk.

Rather than building robots into a humanoid shape (only sometimes useful), allowing them to evolve and discover the ideal form for performing whatever you’ve tasked them to do could prove far more useful. This is particularly true in disaster response, where expensive robots can still be more valuable than humans, but conditions can be very unpredictable and adaptability is key.

Further afield, many futurists imagine “foglets” as the tiny nanobots that will be capable of constructing anything from raw materials, somewhat like the “Santa Claus machine.” But you don’t necessarily need anything quite so indistinguishable from magic to be useful. Programmable matter that can respond and adapt to its surroundings could be used in all kinds of industrial applications. How about a pipe that can strengthen or weaken at will, or divert its direction on command?

We’re some way off from being able to order our beds to turn into bicycles. As with many tech ideas, it may turn out that the traditional low-tech solution is far more practical and cost-effective, even as we can imagine alternatives. But as the march to put a chip in every conceivable object goes on, it seems certain that inanimate objects are about to get a lot more animated.

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