In reality, there already is a robot to be feared in the world today. But its ability to perpetrate harm is entirely driven by humans. It is the military drone: an unmanned bombing machine operated from thousands of miles away. If you are an enemy of the state, your eyes are on the sky, filled with fear. Science fiction drama is reality.

Most people’s idea of the robot is a human-like machine. In reality, a robot is any machine with motors that drive movement and a control system. Today, robots are all around us. The automatic parking system of a modern car is a robot. Sorting conveyors in commercial distribution centers are robots.

The Internet has rendered geographic location nearly irrelevant. Traditional distribution systems have been displaced by ubiquitous, inexpensive methods for the transfer of communication, data and entertainment. For years, the financial industry has systematically woven a powerful network that has nearly eliminated market provincialism. The unbridling of costly distribution systems has given rise to social networking, linking individuals without consideration to cost or physical distance. The individual has the means and the resources to build a network of shared interest around the world. The advent of the portable smart device has converted the Internet from the fixed to the mobile, continuously linking individuals. Web-accessible smart devices with sensors provide data that alter the way the world is perceived as access to stores of data improve individual comprehension or decision-making about circumstances beyond their own senses and knowledge beyond their own memory.

From finance to social to portable access: What is next? As machines continue to develop with embedded micro-processors, the economic value of their linkage via the Internet grows. The Web (or Internet) of Things improves user experience by providing remote control of the machines in our lives. For example, today we can control home security camera motion from our smart phones.

As this expansion continues, the Thing (that is, machine) connected to the Web (as separate from the person connected to the Web) will become more and more capable as embedded controls increase in power and sophistication. Actuation of the machine with sensors (think: robot), coupled with the smart device via remote control, will expand as economic opportunity justifies invention. Breakthroughs in embodied cognitive science demonstrate an inextricable link between physical body and thinking. Our methods for learning are bound and enabled by our physical self. As control systems begin to incorporate these discoveries, the seamless integration of the Web-tethered individual coupled with a sensing-actuated machine, will unleash a social/technological expansion of self. In other words, the remotely actuated machine will become the prosthetic extension of our identity. The machine as a prosthetic can do our bidding, provide assistance without our physical presence, and seamlessly integrate with our sensing and action, redefining who we are. The actuated machine becomes the robot that is us.

As the political, social, emotional, legal and financial definitions of the individual expand beyond our bodies, notions of individual rights developed since the Middle Ages must change to accommodate the expanding control possessed by individuals. The paramount question becomes, in a distributed system of man/machine, where is the locus of control? Traditional ideas of geographically bound governmental systems, where power is partly defined by control of communication and location, are challenged.

We move into a future of uncertainty, where our greatest risk is ourselves. Technology continues to advance faster than we can construct systems to manage the risks. This has always been the case. Not until we understand impact through actual fact can we appreciate the implications. One thing is for certain: The robot will be an extension of us. And, good or bad, we know ourselves. If we are not careful, the question will become is this “1984″ or “Brave New World”? It is time to consider a new model for technological development of robotics and smart devices, not just as tools that people control, but in an integrated manner, taking into consideration the social, cultural, legal and economic dimensions.

David Peters is a member of the Global Agenda Council on Robotics and of a committee of the World Economic Forum and is CEO of Universal Robotics Inc. in Nashville.

This article appeared on page E – 4 of the San Francisco Chronicle

“We decided to follow an open-source model, because if all of these labs have a common research platform for doing robotic surgery, the whole field will be able to advance more quickly,” said Jacob Rosen, associate professor of computer engineering in the Baskin School of Engineering at UCSC and principal investigator on the project.

Rosen and Blake Hannaford, director of the UW Biorobotics Laboratory, lead the research groups that developed the Raven II robotic surgery system and its predecessor, Raven I. A grant from the National Science Foundation funded their work to create seven identical Raven II systems. Hannaford said the systems will be shipped out from UW by the end of January. After they are delivered and installed, all seven systems will be networked together over the Internet for collaborative experiments.

Robotic surgery has the potential to enable new surgical procedures that are less invasive than existing techniques. For some procedures, such as prostate surgery, the use of surgical robots is already standard practice. In addition, telesurgery, in which the surgeon operates a robotic system from a remote location, offers the potential to provide better access to expert care in remote areas and the developing world. Having a network of laboratories working on a common platform will make it easier for researchers to share software, replicate experiments, and collaborate in other ways.

Even though it meant giving competing laboratories the tools that had taken them years to develop, Rosen and Hannaford decided to share the Raven II because it seemed like the best way to move the field forward. “These are the leading labs in the nation in the field of surgical robotics, and with everyone working on the same platform we can more easily share new developments and innovations,” Hannaford said.

According to Rosen, most research on surgical robotics in the United States has focused on developing new software for various commercially available robotic systems. “Academic researchers have had limited access to these proprietary systems. We are changing that by providing high-quality hardware developed within academia. Each lab will start with an identical, fully-operational system, but they can change the hardware and software and share new developments and algorithms, while retaining intellectual property rights for their own innovations,” Rosen said.

The Raven II includes a surgical robot with two robotic arms, a camera for viewing the operational field, and a surgeon-interface system for remote operation of the robot. The system is powerful and precise enough to support research on advanced robotic surgery techniques, including online telesurgery.

In addition to Rosen and Hannaford, UCSC postdoctoral researchers Daniel Glozman and Ji Ma, along with a group of dedicated undergraduate students working in Rosen’s Bionics Lab, played a key role in developing the Raven II. Rosen and Glozman have also developed a Raven IV surgical robotics system, which includes four robotic arms and two cameras. The system enables collaboration between two surgeons working from separate locations and connected over the Internet. – Tim Stephens

The installation involves a fleet of quadrocopters that are programmed to interact, lift, transport and assemble the final tower, all the time receiving commands wirelessly from a local control room. The tower, which will boast a height of 6 meters (19.7 feet) and a diameter of 3.5 meters (11.5 feet), will be constructed within a 10 x 10 x 10 meter (32.8 x 32.8 x 32.8 foot) airspace, in which up to 50 vehicles can be tracked simultaneously at a rate of 370 frames per second with millimeter accuracy. This “Flying Machine Arena” was developed by D’Andrea, and features a state-of-the-art motion capture system.

Each quadrocopter is fitted with custom electronics and onboard sensors to allow for precision vehicle control, whilst also providing the opportunity for pre-programmed flight paths, which could include arcs and spirals. Furthermore, the fleet management technology helps avoid collisions by taking over when the flying robots get too close to each other. The same technology is also used for automating routine take-offs, landings and vehicle calibration and charging.

The Flight Assembled Architecture exhibition will be on display at the FRAC Centre from December 2 through to February 19, 2012.

Check out D’Andrea’s Flying Machine Arena and the impressive quadrocopter moves in the video below.

A much more elegant solution is to teach robots to think and adapt for themselves. Sounds easy, right? Right! I mean, wrong! It’s not easy at all. But once you figure out how to do it, you can plop a robot down anywhere and ask it to do anything and it’ll have a reasonable chance of figuring it out, or at the very least, be able to ask an intelligent question or two to get going.
At Cornell’s Personal Robotics Laboratory, a research group is teaching a robot to generalize groups of objects, which is one of the most basic aspects of reliable adaptability. For example, instead of teaching a robot “this is a cup, and this is a slightly different cup” and so on, you can instead teach a robot to recognize features common to all cups, so that when it sees something cup-like, it can say to itself, “hey, that’s a small container with a handle, I bet it’s a cup!”

This same sort of learning method can also be applied to actions. By teaching a robot how to pick a few different types of cups, the robot can then generalize the lessons and apply them to completely new cups. And if you teach the robot to put a few dishes into a dish rack, it can then use what it knows about the objects and the rack to figure out how to put pretty much anything in there:

If you’re the type who’s impressed by numbers, this robot was able to put unseen objects into the right spot in the dish rack in the right way 92% of the time, which is about 92% better than I do. Touché dishwasher loading robot, touché.

The use of robots, combined with digital design tools, means a new aesthetic becomes possible, with novel shapes and patterns that would be nearly impossible to achieve without the automated machines: industrial manipulators that are extremely precise and good at repetition.

Using robots, the two ETH architects, who run the Gramazio & Kohler design studio, have fabricated intricate building parts out of wood, concrete, bricks, and foam, and have used these parts to build complex, beautiful installations in Zurich, London, Barcelona, New York, and other locations.

The idea of using robotic systems as reconfigurable spaces or “smart furniture” is not new. But the way Gramazio and Kohler are using robots — to actually build large environments — is very innovative indeed. Though their creations thus far are limited in size, the architects are currently exploring the idea of applying robotic fabrication to the design and construction of high-rise buildings.

As you can see in the photos below, the results are impressive. In one of their projects, the architects fitted a manipulator robot in a modified freight container — a “mobile fabrication unit” that could travel anywhere in the world. They took it to Manhattan a few years ago, where the robot built a 22-meter-long (72 feet) brick structure [photos below].

In another project, they used the robot as a milling machine, to create parts that could shape the acoustics of a room [photo below].

Some of their most interesting creations, though, are the ones that use robots to assemble elaborate environments.
Here’s how they describe a 2009 project to build a temporary spatial structure [photo, below] for a major public event in Wettswil am Albis, Switzerland:

The wooden structure consists of 16 contorted elements made from 372 slats. The entire construction is structural support, roof and skin of the building at the same time. The elements were constructed by a digitally controlled robot that cut and precisely placed the slats according to an algorithmic pattern. Each of the elements is individually rotated, producing a progression of subtly varied spaces. The logic of the openings and curvatures as well as the aesthetic details conform to the rules of wood construction. The digital processing bestows a new expression on the traditional wood material.

The architects are also collaborating with roboticists from the ECHORD project to give their robot more mobility. One idea is to use a base with tracks [photo below], and program the robot to recognize its position and surroundings. The biggest challenge is making sure the robot can handle construction tolerances and variations, adapting to changing conditions autonomously.

To learn more, take a look at Gramazio & Kohler’s ETH website and their company website. Below, more photos of their creations and a video discussing their work.

Images: Gramazio & Kohler

Seeing consumer priced sensor entering into the industrial arena is very exciting, as long as the integration can prove to be robust enough.

What high school student could resist the opportunity to use a robot to wreak havoc aboard the International Space Station? And by wreak havoc, I mean compete with other high school students to remotely control a little floating SPHERES robot under the close supervision of a real live astronaut.

The Zero Robotics competition, hosted by NASA and DARPA in cooperation with MIT, Aurora Flight Sciences, and TopCoder, tasks teams of high school students with programming one of those cute little SPHERES microgravity satellite robots to autonomously complete a “technically challenging” three dimensional race against another robot programmed by another team. The programming can be done in a high-level graphical environment (with an instant-gratification Flash-based simulator), and as the teams get more comfortable with things, they can transition directly into C, meaning that the students may actually learn something practical. Woo!

Once the teams have tested their code out in simulation and physically in two dimensions (using robots at MIT), a full 3D simulation tournament will be held, and the top 27 teams from that event will watch live via webcast as the real SPHERES robots execute their code on board the International Space Station in December of this year.

If you want in, or if you’re interested in mentoring a team, you can check out all the details and fill out an application on the Zero Robotics competition website here. The deadline is September 5.

Also, ISS astronauts, if you’re reading this (you do read our blog, right?), you should totally have Robonaut pretend to juggle the SPHERES robots in microgravity and make a video. It would be awesome.

All that other malarkey at the beginning of the vid (you didn’t skip over it, did you?) talks about the programming that goes into making sure that this quadrotor, with what I think we can all agree is a fairly small container, can reliably make catches. Essentially, the robot pays special attention to what’s physically going on with itself, using experience to compensate for thing like increased lift due to ground effect.

This technique is called LBMPC (that’s Learning Based Model Predictive Control), and you can see it in action when the quadrotor needs to move sideways to catch the ball, as it figures in the fact that it’s going to drift a little bit after it cancels out its lateral movement. Clever.

So, if Berkeley’s quadrotor teams up with this robot, this robot, maybe this robot, and of course these robots, you’ve got yourself a halfway decent chance at giving any Little League team a run for their juice boxes, and I for one would pay money to see it happen.

Similarly, your personal robot in the future will need the ability to generalize — for example, to handle your particular set of dishes and put them in your particular dishwasher.

In Cornell’s Personal Robotics Laboratory, a team led by Ashutosh Saxena, assistant professor of computer science, is teaching robots to manipulate objects and find their way around in new environments. They reported two examples of their work at the 2011 Robotics: Science and Systems Conference June 27 at the University of Southern California.

A common thread running through the research is “machine learning” — programming a computer to observe events and find commonalities. With the right programming, for example, a computer can look at a wide array of cups, find their common characteristics and then be able to identify cups in the future. A similar process can teach a robot to find a cup’s handle and grasp it correctly.

Other researchers have gone this far, but Saxena’s team has found that placing objects is harder than picking them up, because there are many options. A cup is placed upright on a table, but upside down in a dishwasher, so the robot must be trained to make those decisions.

“We just show the robot some examples and it learns to generalize the placing strategies and applies them to objects that were not seen before,” Saxena explained. “It learns about stability and other criteria for good placing for plates and cups, and when it sees a new object — a bowl — it applies them.”

In early tests they placed a plate, mug, martini glass, bowl, candy cane, disc, spoon and tuning fork on a flat surface, on a hook, in a stemware holder, in a pen holder and on several different dish racks.

Surveying its environment with a 3-D camera, the robot randomly tests small volumes of space as suitable locations for placement. For some objects it will test for “caging” — the presence of vertical supports that would hold an object upright. It also gives priority to “preferred” locations: A plate goes flat on a table, but upright in a dishwasher.

After training, their robot placed most objects correctly 98 percent of the time when it had seen the objects and environments previously, and 95 percent of the time when working with new objects in a new environment. Performance could be improved, the researchers suggested, by longer training.

But first, the robot has to find the dish rack.

Just as we unconsciously catalog the objects in a room when we walk in, Saxena and colleague Thorsten Joachims, associate professor of computer science, have developed a system that enables a robot to scan a room and identify its objects. Pictures from the robot’s 3-D camera are stitched together to form a 3-D image of an entire room that is then divided into segments, based on discontinuities and distances between objects. The goal is to label each segment.

The researchers trained a robot by giving it 24 office scenes and 28 home scenes in which they had labeled most objects. The computer examines such features as color, texture and what is nearby and decides what characteristics all objects with the same label have in common. In a new environment, it compares each segment of its scan with the objects in its memory and chooses the ones with the best fit.

“The novelty of this work is to learn the contextual relations in 3-D,” Saxena said. “For identifying a keyboard it may be easier to locate the monitors first, because the keyboards are found below the monitors.”

In tests, the robot correctly identified objects about 83 percent of the time in home scenes and 88 percent in offices. In a final test, it successfully located a keyboard in an unfamiliar room. Again, Saxena said, context gives this robot an advantage. The keyboard only shows up as a few pixels in the image, but the monitor is easily found, and the robot uses that information to locate the keyboard.

Robots still have a long way to go to learn like humans, the researchers admit. “I would be really happy if we could build a robot that would even act like a six-month-old baby,” Saxena said.