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#431371 Amazon Is Quietly Building the Robots of ...

Science fiction is the siren song of hard science. How many innocent young students have been lured into complex, abstract science, technology, engineering, or mathematics because of a reckless and irresponsible exposure to Arthur C. Clarke at a tender age? Yet Arthur C. Clarke has a very famous quote: “Any sufficiently advanced technology is indistinguishable from magic.”
It’s the prospect of making that… ahem… magic leap that entices so many people into STEM in the first place. A magic leap that would change the world. How about, for example, having humanoid robots? They could match us in dexterity and speed, perceive the world around them as we do, and be programmed to do, well, more or less anything we can do.
Such a technology would change the world forever.
But how will it arrive? While true sci-fi robots won’t get here right away—the pieces are coming together, and the company best developing them at the moment is Amazon. Where others have struggled to succeed, Amazon has been quietly progressing. Notably, Amazon has more than just a dream, it has the most practical of reasons driving it into robotics.
This practicality matters. Technological development rarely proceeds by magic; it’s a process filled with twists, turns, dead-ends, and financial constraints. New technologies often have to answer questions like “What is this good for, are you being realistic?” A good strategy, then, can be to build something more limited than your initial ambition, but useful for a niche market. That way, you can produce a prototype, have a reasonable business plan, and turn a profit within a decade. You might call these “stepping stone” applications that allow for new technologies to be developed in an economically viable way.
You need something you can sell to someone, soon: that’s how you get investment in your idea. It’s this model that iRobot, developers of the Roomba, used: migrating from military prototypes to robotic vacuum cleaners to become the “boring, successful robot company.” Compare this to Willow Garage, a genius factory if ever there was one: they clearly had ambitions towards a general-purpose, multi-functional robot. They built an impressive device—PR2—and programmed the operating system, ROS, that is still the industry and academic standard to this day.
But since they were unable to sell their robot for much less than $250,000, it was never likely to be a profitable business. This is why Willow Garage is no more, and many workers at the company went into telepresence robotics. Telepresence is essentially videoconferencing with a fancy robot attached to move the camera around. It uses some of the same software (for example, navigation and mapping) without requiring you to solve difficult problems of full autonomy for the robot, or manipulating its environment. It’s certainly one of the stepping-stone areas that various companies are investigating.
Another approach is to go to the people with very high research budgets: the military.
This was the Boston Dynamics approach, and their incredible achievements in bipedal locomotion saw them getting snapped up by Google. There was a great deal of excitement and speculation about Google’s “nightmare factory” whenever a new slick video of a futuristic militarized robot surfaced. But Google broadly backed away from Replicant, their robotics program, and Boston Dynamics was sold. This was partly due to PR concerns over the Terminator-esque designs, but partly because they didn’t see the robotics division turning a profit. They hadn’t found their stepping stones.
This is where Amazon comes in. Why Amazon? First off, they just announced that their profits are up by 30 percent, and yet the company is well-known for their constantly-moving Day One philosophy where a great deal of the profits are reinvested back into the business. But lots of companies have ambition.
One thing Amazon has that few other corporations have, as well as big financial resources, is viable stepping stones for developing the technologies needed for this sort of robotics to become a reality. They already employ 100,000 robots: these are of the “pragmatic, boring, useful” kind that we’ve profiled, which move around the shelves in warehouses. These robots are allowing Amazon to develop localization and mapping software for robots that can autonomously navigate in the simple warehouse environment.
But their ambitions don’t end there. The Amazon Robotics Challenge is a multi-million dollar competition, open to university teams, to produce a robot that can pick and package items in warehouses. The problem of grasping and manipulating a range of objects is not a solved one in robotics, so this work is still done by humans—yet it’s absolutely fundamental for any sci-fi dream robot.
Google, for example, attempted to solve this problem by hooking up 14 robot hands to machine learning algorithms and having them grasp thousands of objects. Although results were promising, the 10 to 20 percent failure rate for grasps is too high for warehouse use. This is a perfect stepping stone for Amazon; should they crack the problem, they will likely save millions in logistics.
Another area where humanoid robotics—especially bipedal locomotion, or walking, has been seriously suggested—is in the last mile delivery problem. Amazon has shown willingness to be creative in this department with their notorious drone delivery service. In other words, it’s all very well to have your self-driving car or van deliver packages to people’s doors, but who puts the package on the doorstep? It’s difficult for wheeled robots to navigate the full range of built environments that exist. That’s why bipedal robots like CASSIE, developed by Oregon State, may one day be used to deliver parcels.
Again: no one more than Amazon stands to profit from cracking this technology. The line from robotics research to profit is very clear.
So, perhaps one day Amazon will have robots that can move around and manipulate their environments. But they’re also working on intelligence that will guide those robots and make them truly useful for a variety of tasks. Amazon has an AI, or at least the framework for an AI: it’s called Alexa, and it’s in tens of millions of homes. The Alexa Prize, another multi-million-dollar competition, is attempting to make Alexa more social.
To develop a conversational AI, at least using the current methods of machine learning, you need data on tens of millions of conversations. You need to understand how people will try to interact with the AI. Amazon has access to this in Alexa, and they’re using it. As owners of the leading voice-activated personal assistant, they have an ecosystem of developers creating apps for Alexa. It will be integrated with the smart home and the Internet of Things. It is a very marketable product, a stepping stone for robot intelligence.
What’s more, the company can benefit from its huge sales infrastructure. For Amazon, having an AI in your home is ideal, because it can persuade you to buy more products through its website. Unlike companies like Google, Amazon has an easy way to make a direct profit from IoT devices, which could fuel funding.
For a humanoid robot to be truly useful, though, it will need vision and intelligence. It will have to understand and interpret its environment, and react accordingly. The way humans learn about our environment is by getting out and seeing it. This is something that, for example, an Alexa coupled to smart glasses would be very capable of doing. There are rumors that Alexa’s AI will soon be used in security cameras, which is an ideal stepping stone task to train an AI to process images from its environment, truly perceiving the world and any threats it might contain.
It’s a slight exaggeration to say that Amazon is in the process of building a secret robot army. The gulf between our sci-fi vision of robots that can intelligently serve us, rather than mindlessly assemble cars, is still vast. But in quietly assembling many of the technologies needed for intelligent, multi-purpose robotics—and with the unique stepping stones they have along the way—Amazon might just be poised to leap that gulf. As if by magic.
Image Credit: Denis Starostin / Shutterstock.com Continue reading

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#431368 This Week’s Awesome Stories From ...

INTERNET OF THINGSAmazon Key Is a New Service That Lets Couriers Unlock Your Front DoorBen Popper | The Verge“When a courier arrives with a package for in-home delivery, they scan the barcode, sending a request to Amazon’s cloud. If everything checks out, the cloud grants permission by sending a message back to the camera, which starts recording. The courier then gets a prompt on their app, swipes the screen, and voilà, your door unlocks.”
ROBOTICSWatch Yamaha’s Humanoid Robot Ride a Motorcycle Around a RacetrackPhilip E. Ross | IEEE Spectrum“What’s striking is that the bike is unmodified: the robot is a hunched-over form on top. It senses the environment, calculates what to do, keeps the bike stable, manages acceleration and deceleration—all while factoring in road conditions, air resistance, and engine braking.”
ARTIFICIAL INTELLIGENCETech Giants Are Paying Huge Salaries for Scarce A.I. TalentCade Metz | The New York Times“Typical A.I. specialists, including both Ph.D.s fresh out of school and people with less education and just a few years of experience, can be paid from $300,000 to $500,000 a year or more in salary and company stock, according to nine people who work for major tech companies or have entertained job offers from them. All of them requested anonymity because they did not want to damage their professional prospects.”
HEALTH This Doctor Diagnosed His Own Cancer With an iPhone UltrasoundAntonio Regalado | MIT Technology Review“The device he used, called the Butterfly IQ, is the first solid-state ultrasound machine to reach the market in the U.S. Ultrasound works by shooting sound into the body and capturing the echoes. Usually, the sound waves are generated by a vibrating crystal. But Butterfly’s machine instead uses 9,000 tiny drums etched onto a semiconductor chip.”
ENTREPRENEURSHIPWeWork: A $20 Billion Startup Fueled by Silicon Valley Pixie DustEliot Brown | Wall Street Journal“WeWork’s strategy carries the costs and risks associated with traditional real estate. Its client list is heavily weighted toward startups that may or may not be around for long. WeWork is on the hook for long-term leases, and it doesn’t own its own buildings. Vacancy rates have risen recently, and the company is increasing incentives to draw tenants… The model has proved popular, with 150,000 individuals renting space in more than 170 locations globally.”
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#431175 Servosila introduces Mobile Robots ...

Servosila introduces a new member of the family of Servosila “Engineer” robots, a UGV called “Radio Engineer”. This new variant of the well-known backpack-transportable robot features a Software Defined Radio (SDR) payload module integrated into the robotic vehicle.

“Several of our key customers had asked us to enable an Electronic Warfare (EW) or Cognitive Radio applications in our robots”, – says a spokesman for the company, “By integrating a Software Defined Radio (SDR) module into our robotic platforms we cater to both requirements. Radio spectrum analysis, radio signal detection, jamming, and radio relay are important features for EOD robots such as ours. Servosila continues to serve the customers by pushing the boundaries of what their Servosila robots can do. Our partners in the research world and academia shall also greatly benefit from the new functionality that gives them more means of achieving their research goals.”
Photo Credit: Servosila – www.servosila.com
Coupling a programmable mobile robot with a software-defined radio creates a powerful platform for developing innovative applications that mix mobility and artificial intelligence with modern radio technologies. The new robotic radio applications include localized frequency hopping pattern analysis, OFDM waveform recognition, outdoor signal triangulation, cognitive mesh networking, automatic area search for radio emitters, passive or active mobile robotic radars, mobile base stations, mobile radio scanners, and many others.

A rotating head of the robot with mounts for external antennae acts as a pan-and-tilt device thus enabling various scanning and tracking applications. The neck of the robotic head is equipped with a pair of highly accurate Servosila-made servos with a pointing precision of 3.0 angular minutes. This means that the robot can point its antennae with an unprecedented accuracy.

Researchers and academia can benefit from the platform’s support for GnuRadio, an open source software framework for developing SDR applications. An on-board Intel i7 computer capable of executing OpenCL code, is internally connected to the SDR payload module. This makes it possible to execute most existing GnuRadio applications directly on the robot’s on-board computer. Other sensors of the robot such as a GPS sensor, an IMU or a thermal vision camera contribute into sensor fusion algorithms.

Since Servosila “Engineer” mobile robots are primarily designed for outdoor use, the SDR module is fully enclosed into a hardened body of the robot which provides protection in case of dust, rain, snow or impacts with obstacles while the robot is on the move. The robot and its SDR payload module are both powered by an on-board battery thus making the entire robotic radio platform independent of external power supplies.

Servosila plans to start shipping the SDR-equipped robots to international customers in October, 2017.

Web: https://www.servosila.com
YouTube: https://www.youtube.com/user/servosila/videos

About the Company
Servosila is a robotics technology company that designs, produces and markets a range of mobile robots, robotic arms, servo drives, harmonic reduction gears, robotic control systems as well as software packages that make the robots intelligent. Servosila provides consulting, training and operations support services to various customers around the world. The company markets its products and services directly or through a network of partners who provide tailored and localized services that meet specific procurement, support or operational needs.
Press Release above is by: Servosila
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#431170 This Week’s Awesome Stories From ...

AUGMENTED REALITY
ZED Mini Turns Rift and Vive Into an AR Headset From the FutureBen Lang | Road to VR“When attached, the camera provides stereo pass-through video and real-time depth and environment mapping, turning the headsets into dev kits emulating the capabilities of high-end AR headsets of the future. The ZED Mini will launch in November.”
ROBOTICS
Life-Size Humanoid Robot Is Designed to Fall Over (and Over and Over)Evan Ackerman | IEEE Spectrum “The researchers came up with a new strategy for not worrying about falls: not worrying about falls. Instead, they’ve built their robot from the ground up with an armored structure that makes it totally okay with falling over and getting right back up again.”
SPACE
Russia Will Team up With NASA to Build a Lunar Space StationAnatoly Zak | Popular Mechanics “NASA and its partner agencies plan to begin the construction of the modular habitat known as the Deep-Space Gateway in orbit around the Moon in the early 2020s. It will become the main destination for astronauts for at least a decade, extending human presence beyond the Earth’s orbit for the first time since the end of the Apollo program in 1972. Launched on NASA’s giant SLS rocket and serviced by the crews of the Orion spacecraft, the outpost would pave the way to a mission to Mars in the 2030s.”
TRANSPORTATION
Dubai Starts Testing Crewless Two-Person ‘Flying Taxis’Thuy Ong | The Verge“The drone was uncrewed and hovered 200 meters high during the test flight, according to Reuters. The AAT, which is about two meters high, was supplied by specialist German manufacturer Volocopter, known for its eponymous helicopter drone hybrid with 18 rotors…Dubai has a target for autonomous transport to account for a quarter of total trips by 2030.”
AUTONOMOUS CARS
Toyota Is Trusting a Startup for a Crucial Part of Its Newest Self-Driving CarsJohana Bhuiyan | Recode “Toyota unveiled the next generation of its self-driving platform today, which features more accurate object detection technology and mapping, among other advancements. These test cars—which Toyota is testing on both a closed driving course and on some public roads—will also be using Luminar’s lidar sensors, or radars that use lasers to detect the distance to an object.”
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#431165 Intel Jumps Into Brain-Like Computing ...

The brain has long inspired the design of computers and their software. Now Intel has become the latest tech company to decide that mimicking the brain’s hardware could be the next stage in the evolution of computing.
On Monday the company unveiled an experimental “neuromorphic” chip called Loihi. Neuromorphic chips are microprocessors whose architecture is configured to mimic the biological brain’s network of neurons and the connections between them called synapses.
While neural networks—the in vogue approach to artificial intelligence and machine learning—are also inspired by the brain and use layers of virtual neurons, they are still implemented on conventional silicon hardware such as CPUs and GPUs.
The main benefit of mimicking the architecture of the brain on a physical chip, say neuromorphic computing’s proponents, is energy efficiency—the human brain runs on roughly 20 watts. The “neurons” in neuromorphic chips carry out the role of both processor and memory which removes the need to shuttle data back and forth between separate units, which is how traditional chips work. Each neuron also only needs to be powered while it’s firing.

At present, most machine learning is done in data centers due to the massive energy and computing requirements. Creating chips that capture some of nature’s efficiency could allow AI to be run directly on devices like smartphones, cars, and robots.
This is exactly the kind of application Michael Mayberry, managing director of Intel’s research arm, touts in a blog post announcing Loihi. He talks about CCTV cameras that can run image recognition to identify missing persons or traffic lights that can track traffic flow to optimize timing and keep vehicles moving.
There’s still a long way to go before that happens though. According to Wired, so far Intel has only been working with prototypes, and the first full-size version of the chip won’t be built until November.
Once complete, it will feature 130,000 neurons and 130 million synaptic connections split between 128 computing cores. The device will be 1,000 times more energy-efficient than standard approaches, according to Mayberry, but more impressive are claims the chip will be capable of continuous learning.
Intel’s newly launched self-learning neuromorphic chip.
Normally deep learning works by training a neural network on giant datasets to create a model that can then be applied to new data. The Loihi chip will combine training and inference on the same chip, which will allow it to learn on the fly, constantly updating its models and adapting to changing circumstances without having to be deliberately re-trained.
A select group of universities and research institutions will be the first to get their hands on the new chip in the first half of 2018, but Mayberry said it could be years before it’s commercially available. Whether commercialization happens at all may largely depend on whether early adopters can get the hardware to solve any practically useful problems.
So far neuromorphic computing has struggled to gain traction outside the research community. IBM released a neuromorphic chip called TrueNorth in 2014, but the device has yet to showcase any commercially useful applications.
Lee Gomes summarizes the hurdles facing neuromorphic computing excellently in IEEE Spectrum. One is that deep learning can run on very simple, low-precision hardware that can be optimized to use very little power, which suggests complicated new architectures may struggle to find purchase.
It’s also not easy to transfer deep learning approaches developed on conventional chips over to neuromorphic hardware, and even Intel Labs chief scientist Narayan Srinivasa admitted to Forbes Loihi wouldn’t work well with some deep learning models.
Finally, there’s considerable competition in the quest to develop new computer architectures specialized for machine learning. GPU vendors Nvidia and AMD have pivoted to take advantage of this newfound market and companies like Google and Microsoft are developing their own in-house solutions.
Intel, for its part, isn’t putting all its eggs in one basket. Last year it bought two companies building chips for specialized machine learning—Movidius and Nervana—and this was followed up with the $15 billion purchase of self-driving car chip- and camera-maker Mobileye.
And while the jury is still out on neuromorphic computing, it makes sense for a company eager to position itself as the AI chipmaker of the future to have its fingers in as many pies as possible. There are a growing number of voices suggesting that despite its undoubted power, deep learning alone will not allow us to imbue machines with the kind of adaptable, general intelligence humans possess.
What new approaches will get us there are hard to predict, but it’s entirely possible they will only work on hardware that closely mimics the one device we already know is capable of supporting this kind of intelligence—the human brain.
Image Credit: Intel Continue reading

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