INB 201Global Business TechnologyDr. LairsonRobotics

Bipedal Robot 4/11/2016

https://www.youtube.com/watch?v=iyZE0psQsX0

TARS from Interstellar

Why is DARPA willing to spend $80 million to hold a Robotics Grand Challenge?

The agency made robots a priority because, like many others, it suspects that the technology may be on the cusp of scaling far greater heights than a nine-step aluminium ladder. It is expressing its support in the unusual, quasi-sporting, highly public forum of the DRC because robotics is a technology unlike any other. As machines that sense their environment, analyse it and respond accordingly, robots lend themselves to showmanship, judged as they are by their actions in the world (this special report will deal only glancingly with other machines sometimes called robots that do not have a moving physical presence, such as software “bots” or stationary bits of automation). They exert a fascination, both on their designers and their fans, that transcends the technology’s current practical uses.

Technology Readiness Level

Is DARPA right?

although computers can easily do some things people find hard (such as playing chess), they cannot fathom many things people can do without thinking. Getting robots to walk moderately well has taken decades and many hundreds of millions of dollars, mostly spent in Japan. To get a (non-walking) PR2 simply to recognise a thing that needs picking up still takes a lot of work. In Masayuki Inaba’s laboratory at the University of Tokyo, where some of the SCHAFT team got their start, a PR2 programmed by gifted students tried to serve your correspondent a can of coffee from a fridge. It opened the fridge door and got the coffee out, but then tried to serve the can to the fridge instead.

The robots that did best in Florida will reconvene in late 2014 or early 2015 for the finals, where the tasks will be harder and performance, everyone hopes, better. Mr Pratt explains that one purpose of the DARPA challenge is to give a sense of how much robotic progress a year of research and funding can buy. A previous competition proved wildly successful at promoting progress in a related field. The first DARPA Grand Challenge, in 2004, required teams to get cars to drive themselves over a 240km desert route. None of them made it even a 20th of the distance. Yet when the race was reconvened a year later, better software for mapping and understanding the world allowed five competitors to complete the course. In the exhibition ground at the DRC sat one of Google’s driverless cars, its ancestry directly traceable to the winning team in that second challenge. If such progress is possible on the roads, why not in the kitchen, the retirement home or the shopping mall?

The DARPA Robotics Challenge Was A Bust

Why DARPA Needs To Stage Another Bot Contest, With Even More Pratfalls

By Erik Sofge Posted July 6, 2015

DARPA Robotics Challenge: Amazing Moments, Lessons Learned, and What's NextBy Evan Ackerman and Erico Guizzo Posted 11 Jun 2015 | 17:45 GMT

7 Things We Learned After the DARPA Robotics Challenge FinalsThe DARPA Robotics Challenge Finals have come and gone. Here are some of the most important takeaways from the ultimate robot competition.

1. Our Expectations Were Too High

There’s been a lot of talk about how underwhelming the DRC Finals were. Myself included. How come each robot couldn’t complete all eight tasks flawlessly? Look how often the robots fell. Why did it take the winning team so long (44 minutes, 28 seconds) to complete the obstacle course?

2. DRC-Hubo Should be Baseline Robot for Future Challenges

Team KAIST from South Korea dominated the competition, performing all eight tasks flawlessly in a time of 44 minutes, 28 seconds to win the $2 million grand prize. The second-place finisher, Team IHMC Robotics, completed eight tasks in 50 minutes, 26 seconds, and Carnegie Mellon University¹s Tartan Rescue Team came in third with a time of 55 minutes, 15 seconds.

4. Walking is Still Tough for Robots

Just three out of 23 teams managed to complete all eight tasks set for the robots, which included driving and exiting a vehicle, opening and going through a door, locating and opening a valve, using a tool to cut a hole in a wall, removing an electrical plug from a socket and putting it in a different socket, traversing rubble and climbing stairs.

5. Robot Competitions Are Here to Stay

No matter what your opinion is of the DRC Finals, robot competitions aren’t going anywhere anytime soon. There are some serious issues - aging populations, natural disasters, dwindling natural resources - that robotics can certainly help.

6. Robot Autonomy Remains Low

“The state of autonomy in robotics these days is actually only beginning, and so the level of autonomy we’re talking about is at the task level, not at the mission level,

7. The DARPA Challenge demonstrates how far the globalization of advanced knowledge has come

Videos of tasks

http://spectrum.ieee.org/automaton/robotics/humanoids/how-kaist-drc-hubo-won-darpa-robotics-challenge

The MIT DARPA Robotics Challenge

http://drc.mit.edu/

For now, robots operate much better as extension of humans not as replacements.

Why will DARPA continue to develop humanoid robots?

Hardware and software advances:

Hardware

Take the Kinect sensor developed at Microsoft for the company’s Xbox 360 game console, released in 2010. An array of microphones and cameras are artfully combined with high-powered chips and well-tailored software to sense players’ movements from a distance and apply them to the game.

Used in robotics, the Kinect sensor is a cheap, easy and fairly reliable way to provide both a sense of depth and a kind of “person-detector”, which is a great help to robots that need to map and navigate their surroundings. Most robot laboratories have some version of it, either bolted to a robot or mounted on the wall or ceiling. A shopping mall in Osaka is wired up with the sensors set up by Japan’s Advanced Telecommunications Research Institute International to tell robots where they are and how to spot the shoppers to whom they are learning to give leaflets and directions.

As well as having access to better sensors and processors from other fields, robotics has devised its own new ways of making software for them.

Software

a test bed for the Robot Operating System (ROS), a uniform way of passing messages between the various software routines that run a robot. ROS, now looked after by a not-for-profit spin-off from Willow Garage, the Open Source Robotics Foundation (OSRF), is free to use and easily customised, and is being taken up by more and more researchers, many of whom happily share their fixes to the software. Using an ROS navigation “stack” and a Kinect, it is now relatively easy to build a rudimentary robot capable of finding its way round a building—call it a “trundlebot”—though making it robust and reliable is a lot harder.

Lower barriers to entry

Robotics used to be hard to do because to make even a poor robot you had to be good at a whole lot of different things: artificial intelligence, building manipulators, engineering joints and wheels, electronics and so on. As a result, academic robotics research has generally been concentrated at universities that already have a flourishing robotics programme with capabilities across the board, such as Carnegie Mellon, MIT and the University of Tokyo. Now a small team with a fresh insight in a single area—making hands, say, or machine-learning—can use ROS and reasonably cheap hardware to put together a robotic system on which to try out its ideas without being expert in any of the other areas involved.

Role of 3D printers

The biggest cluster, that around MIT, is home not only to Boston Dynamics, to date supported mostly by military R&D contracts, but also to iRobot, which has shown the way in developing a profitable service-robotics business aimed at consumers.

But, Robotics is close to developing like the early personal computers – from garages

Military drones

Buy Raven RQ-11

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Uber and Carnegie Mellon

Carnegie Mellon’s CHIMP robot, assisted by the roboticist Prathamesh Kini, demonstrating its fine motor skills.3rd place finisher in the 2015 DARPA Robotics Challenge

There was something missing from the lab today, though. In February, a group of some 40 employees — including several longtime senior lab members — resigned. They had been lured away, en masse, by a new employer: Uber.

The San Francisco firm, famous for its popular car-dispatch services, is getting into the robotics business itself. In February, barely a mile away, it opened the Advanced Technologies Center in Pittsburgh, where the former university researchers are now developing technologies to help Uber extend its reach over the roads. These include producing better maps and safer guidance systems — and most lucrative, even if they’re still years away, Uber’s very own fleet of self-driving cars.

It was a startling raid of talent. By offering private-sector salaries substantially higher than university equivalents (as well as a chance to earn equity in a fast-rising tech firm), Uber was able to acquire a hefty chunk of the center’s brain trust, including some top experts in autonomous vehicles. In doing so, the company has placed a bet that self-driving robots are no longer the stuff of scholarly visions but valuable intellectual property.

There’s a useful high-tech concept called the Technology Readiness Level that helps explain why Uber pounced when it did. NASA came up with this scale to gauge the maturity of a given field of applied science. At Level 1, an area of scientific inquiry is so new that nobody understands its basic principles. At Level 9, the related technology is so mature it’s ready to be used in commercial products. ‘‘Basically, 1 is like Newton figuring out the laws of gravity, and 9 is you’ve been launching

rockets into space, constantly and reliably,’’ says Jeff Legault, the director of strategic business development at the National Robotics Engineering Center.

These days, private industry gets involved mostly when a field of research has matured to the midpoint of the NASA scale. In the ’90s and early ’00s this happened to ‘‘machine learning,’’ the science of getting machines to recognize patterns. It had long been an academic concern. But once online firms like Google began grappling with ‘‘big data’’ — search-engine requests, social-network behavior, email — the field became suddenly lucrative, and Silicon Valley started frantically hiring experts away from Stanford.

In 1979, the university founded its Robotics Institute to tackle the basic problems in the field, like how to interpret sensor data so a robot could ‘‘see.’’ But by the ’80s, government agencies and private firms struggling to create industrial and military robots were asking Carnegie Mellon’s roboticists for help. To capitalize on this demand, the school established its National Robotics Engineering Center in 1995 and staffed it with a few faculty members and a large complement of full-time engineers, often young robotics graduates.

In effect, Carnegie Mellon used the NASA scale to carve up its robotics research. The Robotics Institute would handle research from Levels 1 to 3 or 4, while the center would take technology from there and move it to 7. If John Deere approached the center for help with a self-driving tractor, for example, the center would produce a prototype that could be mass-produced while publishing its research publicly.

By the late 2000s, the field of “wheeled robots” was taking off. Google had started its effort to make self-driving cars, and in 2007 roboticists at Carnegie Mellon won a military competition to produce a car that could navigate city streets; automakers were developing systems to help drivers stay in their lanes. A sort of Silicon Valley of robotics began to emerge in Pittsburgh as investors funneled money to firms like 4Moms (which makes robotized strollers) and Aethon (robots that deliver supplies in hospitals), heavily staffed by former Carnegie Mellon robotics experts. Today, nearly a third of the university’s faculty in the field are involved in a start-up on the side.

Back at the lab, in fact, a group was already preparing to break away. One of Uber’s recent hires from the National Robotics Engineering Center told me that he and his 20-person team had long found their lab work rewarding, but that they had been stunned by the rush of money into robotics in recent years: Google bought Nest, the Internet-connected thermostat firm, for $3.2 billion; Makerbot, a start-up that creates inexpensive 3-D printers, was acquired by another company for $604 million. ‘‘We were just flabbergasted,’’ he says, adding that his team could have invented those products ‘‘in two weeks.’’

Many familiar with Carnegie Mellon say the raid had a silver living, because it signals that the university is a hot place to work. One of those is Richard Florida, the urban-studies scholar who argues for the economic role of the ‘‘creative class’’ in a city’s economy. Florida himself was a social scientist at Carnegie Mellon for years, and his

2002 book, ‘‘The Rise of the Creative Class,’’ was inspired in part by his noticing, in the ’90s, that engineers there with a good high-tech idea had to leave for the East or West Coast to find investors, and they rarely came back. ‘‘Pittsburgh has always been this caldron, but they couldn’t get scale,’’ Florida says. ‘‘What Uber provides is immediate scale.’’ And funding: This month, the company announced a $5.5 million gift to Carnegie Mellon to support a new robotics chair and three fellowships.

Where does this leave CM?

Moore says there is still blue-sky work to be done in robotics. One example is the problem of ‘‘grasping.’’ Sure, wheeled robots can steer themselves through an Amazon shipping center pretty accurately; a self-driving car can trundle around San Francisco without running people over. But no robot can yet match the dexterity of a human hand. It can’t fluidly and confidently manipulate objects on a table — like picking up a coffee mug (assuming it can first identify it). This is, Moore notes, a huge research challenge in the humanitarian and health fields, because such robots could transform the lives of people with mobility issues, including both the elderly and people with spinal-cord injuries.

‘‘There’s maybe about one million people in the United States who, if they dropped their TV remote on the floor, just have to wait for a caregiver to come along and pick it up for them,’’ Moore says. Grasping is a classic early-stage technology, one that the Ubers of the world won’t spend any time developing because the payoffs are too far away. Moore says he also wants this research done in a university setting so the results will be published openly and benefit the world at large.