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#436065 From Mainframes to PCs: What Robot ...

This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE.

Autonomous robots are coming around slowly. We already got autonomous vacuum cleaners, autonomous lawn mowers, toys that bleep and blink, and (maybe) soon autonomous cars. Yet, generation after generation, we keep waiting for the robots that we all know from movies and TV shows. Instead, businesses seem to get farther and farther away from the robots that are able to do a large variety of tasks using general-purpose, human anatomy-inspired hardware.

Although these are the droids we have been looking for, anything that came close, such as Willow Garage’s PR2 or Rethink Robotics’ Baxter has bitten the dust. With building a robotic company being particularly hard, compounding business risk with technological risk, the trend goes from selling robots to selling actual services like mowing your lawn, provide taxi rides, fulfilling retail orders, or picking strawberries by the pound. Unfortunately for fans of R2-D2 and C-3PO, these kind of business models emphasize specialized, room- or fridge-sized hardware that is optimized for one very specific task, but does not contribute to a general-purpose robotic platform.

We have actually seen something very similar in the personal computer (PC) industry. In the 1950s, even though computers could be as big as an entire room and were only available to a selected few, the public already had a good idea of what computers would look like. A long list of fictional computers started to populate mainstream entertainment during that time. In a 1962 New York Times article titled “Pocket Computer to Replace Shopping List,” visionary scientist John Mauchly stated that “there is no reason to suppose the average boy or girl cannot be master of a personal computer.”

In 1968, Douglas Engelbart gave us the “mother of all demos,” browsing hypertext on a graphical screen and a mouse, and other ideas that have become standard only decades later. Now that we have finally seen all of this, it might be helpful to examine what actually enabled the computing revolution to learn where robotics is really at and what we need to do next.

The parallels between computers and robots

In the 1970s, mainframes were about to be replaced by the emerging class of mini-computers, fridge-sized devices that cost less than US $25,000 ($165,000 in 2019 dollars). These computers did not use punch-cards, but could be programmed in Fortran and BASIC, dramatically expanding the ease with which potential applications could be created. Yet it was still unclear whether mini-computers could ever replace big mainframes in applications that require fast and efficient processing of large amounts of data, let alone enter every living room. This is very similar to the robotics industry right now, where large-scale factory robots (mainframes) that have existed since the 1960s are seeing competition from a growing industry of collaborative robots that can safely work next to humans and can easily be installed and programmed (minicomputers). As in the ’70s, applications for these devices that reach system prices comparable to that of a luxury car are quite limited, and it is hard to see how they could ever become a consumer product.

Yet, as in the computer industry, successful architectures are quickly being cloned, driving prices down, and entirely new approaches on how to construct or program robotic arms are sprouting left and right. Arm makers are joined by manufacturers of autonomous carts, robotic grippers, and sensors. These components can be combined, paving the way for standard general purpose platforms that follow the model of the IBM PC, which built a capable, open architecture relying as much on commodity parts as possible.

General purpose robotic systems have not been successful for similar reasons that general purpose, also known as “personal,” computers took decades to emerge. Mainframes were custom-built for each application, while typewriters got smarter and smarter, not really leaving room for general purpose computers in between. Indeed, given the cost of hardware and the relatively little abilities of today’s autonomous robots, it is almost always smarter to build a special purpose machine than trying to make a collaborative mobile manipulator smart.

A current example is e-commerce grocery fulfillment. The current trend is to reserve underutilized parts of a brick-and-mortar store for a micro-fulfillment center that stores goods in little crates with an automated retrieval system and a (human) picker. A number of startups like Alert Innovation, Fabric, Ocado Technology, TakeOff Technologies, and Tompkins Robotics, to just name a few, have raised hundreds of millions of venture capital recently to build mainframe equivalents of robotic fulfillment centers. This is in contrast with a robotic picker, which would drive through the aisles to restock and pick from shelves. Such a robotic store clerk would come much closer to our vision of a general purpose robot, but would require many copies of itself that crowd the aisles to churn out hundreds of orders per hour as a microwarehouse could. Although eventually more efficient, the margins in retail are already low and make it unlikely that this industry will produce the technological jump that we need to get friendly C-3POs manning the aisles.

Startups have raised hundreds of millions of venture capital recently to build mainframe equivalents of robotic fulfillment centers. This is in contrast with a robotic picker, which would drive through the aisles to restock and pick from shelves, and would come much closer to our vision of a general purpose robot.

Mainframes were also attacked from the bottom. Fascination with the new digital technology has led to a hobbyist movement to create microcomputers that were sold via mail order or at RadioShack. Initially, a large number of small businesses was selling tens, at most hundreds, of devices, usually as a kit and with wooden enclosures. This trend culminated into the “1977 Trinity” in the form of the Apple II, the Commodore PET, and the Tandy TRS-80, complete computers that were sold for prices around $2500 (TRS) to $5000 (Apple) in today’s dollars. The main application of these computers was their programmability (in BASIC), which would enable consumers to “learn to chart your biorhythms, balance your checking account, or even control your home environment,” according to an original Apple advertisement. Similarly, there exists a myriad of gadgets that explore different aspects of robotics such as mobility, manipulation, and entertainment.

As in the fledgling personal computing industry, the advertised functionality was at best a model of the real deal. A now-famous milestone in entertainment robotics was the original Sony’s Aibo, a robotic dog that was advertised to have many properties that a real dog has such as develop its own personality, play with a toy, and interact with its owner. Released in 1999, and re-launched in 2018, the platform has a solid following among hobbyists and academics who like its programmability, but probably only very few users who accept the device as a pet stand-in.

There also exist countless “build-your-own-robotic-arm” kits. One of the more successful examples is the uArm, which sells for around $800, and is advertised to perform pick and place, assembly, 3D printing, laser engraving, and many other things that sound like high value applications. Using compelling videos of the robot actually doing these things in a constrained environment has led to two successful crowd-funding campaigns, and have established the robot as a successful educational tool.

Finally, there exist platforms that allow hobbyist programmers to explore mobility to construct robots that patrol your house, deliver items, or provide their users with telepresence abilities. An example of that is the Misty II. Much like with the original Apple II, there remains a disconnect between the price of the hardware and the fidelity of the applications that were available.

For computers, this disconnect began to disappear with the invention of the first electronic spreadsheet software VisiCalc that spun out of Harvard in 1979 and prompted many people to buy an entire microcomputer just to run the program. VisiCalc was soon joined by WordStar, a word processing application, that sold for close to $2000 in today’s dollars. WordStar, too, would entice many people to buy the entire hardware just to use the software. The two programs are early examples of what became known as “killer application.”

With factory automation being mature, and robots with the price tag of a minicomputer being capable of driving around and autonomously carrying out many manipulation tasks, the robotics industry is somewhere where the PC industry was between 1973—the release of the Xerox Alto, the first computer with a graphical user interface, mouse, and special software—and 1979—when microcomputers in the under $5000 category began to take off.

Killer apps for robots
So what would it take for robotics to continue to advance like computers did? The market itself already has done a good job distilling what the possible killer apps are. VCs and customers alike push companies who have set out with lofty goals to reduce their offering to a simple value proposition. As a result, companies that started at opposite ends often converge to mirror images of each other that offer very similar autonomous carts, (bin) picking, palletizing, depalletizing, or sorting solutions. Each of these companies usually serves a single application to a single vertical—for example bin-picking clothes, transporting warehouse goods, or picking strawberries by the pound. They are trying to prove that their specific technology works without spreading themselves too thin.

Very few of these companies have really taken off. One example is Kiva Systems, which turned into the logistic robotics division of Amazon. Kiva and others are structured around sound value propositions that are grounded in well-known user needs. As these solutions are very specialized, however, it is unlikely that they result into any economies of scale of the same magnitude that early computer users who bought both a spreadsheet and a word processor application for their expensive minicomputer could enjoy. What would make these robotic solutions more interesting is when functionality becomes stackable. Instead of just being able to do bin picking, palletizing, and transportation with the same hardware, these three skills could be combined to model entire processes.

A skill that is yet little addressed by startups and is historically owned by the mainframe equivalent of robotics is assembly of simple mechatronic devices. The ability to assemble mechatronic parts is equivalent to other tasks such as changing a light bulb, changing the batteries in a remote control, or tending machines like a lever-based espresso machine. These tasks would involve the autonomous execution of complete workflows possible using a single machine, eventually leading to an explosion of industrial productivity across all sectors. For example, picking up an item from a bin, arranging it on the robot, moving it elsewhere, and placing it into a shelf or a machine is a process that equally applies to a manufacturing environment, a retail store, or someone’s kitchen.

Image: Robotic Materials Inc.

Autonomous, vision and force-based assembly of the
Siemens robot learning challenge.

Even though many of the above applications are becoming possible, it is still very hard to get a platform off the ground without added components that provide “killer app” value of their own. Interesting examples are Rethink Robotics or the Robot Operating System (ROS). Rethink Robotics’ Baxter and Sawyer robots pioneered a great user experience (like the 1973 Xerox Alto, really the first PC), but its applications were difficult to extend beyond simple pick-and-place and palletizing and depalletizing items.

ROS pioneered interprocess communication software that was adapted to robotic needs (multiple computers, different programming languages) and the idea of software modularity in robotics, but—in the absence of a common hardware platform—hasn’t yet delivered a single application, e.g. for navigation, path planning, or grasping, that performs beyond research-grade demonstration level and won’t get discarded once developers turn to production systems. At the same time, an increasing number of robotic devices, such as robot arms or 3D perception systems that offer intelligent functionality, provide other ways to wire them together that do not require an intermediary computer, while keeping close control over the real-time aspects of their hardware.

Image: Robotic Materials Inc.

Robotic Materials GPR-1 combines a MIR-100 autonomous cart with an UR-5 collaborative robotic arm, an onRobot force/torque sensor and Robotic Materials’ SmartHand to perform out-of-the-box mobile assembly, bin picking, palletizing, and depalletizing tasks.

At my company, Robotic Materials Inc., we have made strides to identify a few applications such as bin picking and assembly, making them configurable with a single click by combining machine learning and optimization with an intuitive user interface. Here, users can define object classes and how to grasp them using a web browser, which then appear as first-class objects in a robot-specific graphical programming language. We have also done this for assembly, allowing users to stack perception-based picking and force-based assembly primitives by simply dragging and dropping appropriate commands together.

While such an approach might answer the question of a killer app for robots priced in the “minicomputer” range, it is unclear how killer app-type value can be generated with robots in the less-than-$5000 category. A possible answer is two-fold: First, with low-cost arms, mobility platforms, and entertainment devices continuously improving, a confluence of technology readiness and user innovation, like with the Apple II and VisiCalc, will eventually happen. For example, there is not much innovation needed to turn Misty into a home security system; the uArm into a low-cost bin-picking system; or an Aibo-like device into a therapeutic system for the elderly or children with autism.

Second, robots and their components have to become dramatically cheaper. Indeed, computers have seen an exponential reduction in price accompanied by an exponential increase in computational power, thanks in great part to Moore’s Law. This development has helped robotics too, allowing us to reach breakthroughs in mobility and manipulation due to the ability to process massive amounts of image and depth data in real-time, and we can expect it to continue to do so.

Is there a Moore’s Law for robots?
One might ask, however, how a similar dynamics might be possible for robots as a whole, including all their motors and gears, and what a “Moore’s Law” would look like for the robotics industry. Here, it helps to remember that the perpetuation of Moore’s Law is not the reason, but the result of the PC revolution. Indeed, the first killer apps for bookkeeping, editing, and gaming were so good that they unleashed tremendous consumer demand, beating the benchmark on what was thought to be physically possible over and over again. (I vividly remember 56 kbps to be the absolute maximum data rate for copper phone lines until DSL appeared.)

That these economies of scale are also applicable to mechatronics is impressively demonstrated by the car industry. A good example is the 2020 Prius Prime, a highly computerized plug-in hybrid, that is available for one third of the cost of my company’s GPR-1 mobile manipulator while being orders of magnitude more complex, sporting an electrical motor, a combustion engine, and a myriad of sensors and computers. It is therefore very well conceivable to produce a mobile manipulator that retails at one tenth of the cost of a modern car, once robotics enjoy similar mass-market appeal. Given that these robots are part of the equation, actively lowering cost of production, this might happen as fast as never before in the history of industrialization.

It is therefore very well conceivable to produce a mobile manipulator that retails at one tenth of the cost of a modern car, once robotics enjoy similar mass-market appeal.

There is one more driver that might make robots exponentially more capable: the cloud. Once a general purpose robot has learned or was programmed with a new skill, it could share it with every other robot. At some point, a grocer who buys a robot could assume that it already knows how to recognize and handle 99 percent of the retail items in the store. Likewise, a manufacturer can assume that the robot can handle and assemble every item available from McMaster-Carr and Misumi. Finally, families could expect a robot to know every kitchen item that Ikea and Pottery Barn is selling. Sounds like a labor intense problem, but probably more manageable than collecting footage for Google’s Street View using cars, tricycles, and snowmobiles, among other vehicles.

Strategies for robot startups
While we are waiting for these two trends—better and better applications and hardware with decreasing cost—to converge, we as a community have to keep exploring what the canonical robotic applications beyond mobility, bin picking, palletizing, depalletizing, and assembly are. We must also continue to solve the fundamental challenges that stand in the way of making these solutions truly general and robust.

For both questions, it might help to look at the strategies that have been critical in the development of the personal computer, which might equally well apply to robotics:

Start with a solution to a problem your customers have. Unfortunately, their problem is almost never that they need your sensor, widget, or piece of code, but something that already costs them money or negatively affects them in some other way. Example: There are many more people who had a problem calculating their taxes (and wanted to buy VisiCalc) than writing their own solution in BASIC.

Build as little of your own hardware as necessary. Your business model should be stronger than the margin you can make on the hardware. Why taking the risk? Example: Why build your own typewriter if you can write the best typewriting application that makes it worth buying a computer just for that?

If your goal is a platform, make sure it comes with a killer application, which alone justifies the platform cost. Example: Microcomputer companies came and went until the “1977 Trinity” intersected with the killer apps spreadsheet and word processors. Corollary: You can also get lucky.

Use an open architecture, which creates an ecosystem where others compete on creating better components and peripherals, while allowing others to integrate your solution into their vertical and stack it with other devices. Example: Both the Apple II and the IBM PC were completely open architectures, enabling many clones, thereby growing the user and developer base.

It’s worthwhile pursuing this. With most business processes already being digitized, general purpose robots will allow us to fill in gaps in mobility and manipulation, increasing productivity at levels only limited by the amount of resources and energy that are available, possibly creating a utopia in which creativity becomes the ultimate currency. Maybe we’ll even get R2-D2.

Nikolaus Correll is an associate professor of computer science at the University of Colorado at Boulder where he works on mobile manipulation and other robotics applications. He’s co-founder and CTO of Robotic Materials Inc., which is supported by the National Science Foundation and the National Institute of Standards and Technology via their Small Business Innovative Research (SBIR) programs. Continue reading

Posted in Human Robots

#436021 AI Faces Speed Bumps and Potholes on Its ...

Implementing machine learning in the real world isn’t easy. The tools are available and the road is well-marked—but the speed bumps are many.

That was the conclusion of panelists wrapping up a day of discussions at the IEEE AI Symposium 2019, held at Cisco’s San Jose, Calif., campus last week.

The toughest problem, says Ben Irving, senior manager of Cisco’s strategy innovations group, is people.

It’s tough to find data scientist expertise, he indicated, so companies are looking into non-traditional sources of personnel, like political science. “There are some untapped areas with a lot of untapped data science expertise,” Irving says.

Lazard’s artificial intelligence manager Trevor Mottl agreed that would-be data scientists don’t need formal training or experience to break into the field. “This field is changing really rapidly,” he says. “There are new language models coming out every month, and new tools, so [anyone should] expect to not know everything. Experiment, try out new tools and techniques, read, study, spend time; there aren’t any true experts at this point because the foundational elements are shifting so rapidly.”

“It is a wonderful time to get into a field,” he reasons, noting that it doesn’t take long to catch up because there aren’t 20 years of history.”

Confusion about what different kinds of machine learning specialists do doesn’t help the personnel situation. An audience member asked panelists to explain the difference between data scientist, data analyst, and data engineer. Darrin Johnson, Nvidia global director of technical marketing for enterprise, admitted it’s hard to sort out, and any two companies could define the positions differently. “Sometimes,” he says, particularly at smaller companies, “a data scientist plays all three roles. But as companies grow, there are different groups that ingest data, clean data, and use data. At some companies, training and inference are separate. It really depends, which is a challenge when you are trying to hire someone.”

Mitigating the risks of a hot job market

The competition to hire data scientists, analysts, engineers, or whatever companies call them requires that managers make sure any work being done is structured and comprehensible at all times, the panelists cautioned.

“We need to remember that our data scientists go home every day and sometimes they don’t come back because they go home and then go to a different company,” says Lazard’s Mottl. “That’s a fact of life. If you give people choice on [how they do development], and have a successful person who gets poached by competitor, you have to either hire a team to unwrap what that person built or jettison their work and rebuild it.”

By contrast, he says, “places that have structured coding and structured commits and organized constructions of software have done very well.”

But keeping all of a company’s engineers working with the same languages and on the same development paths is not easy to do in a field that moves as fast as machine learning. Zongjie Diao, Cisco director of product management for machine learning, quipped: “I have a data scientist friend who says the speed at which he changes girlfriends is less than speed at which he changes languages.”

The data scientist/IT manager clash

Once a company finds the data engineers and scientists they need and get them started on the task of applying machine learning to that company’s operations, one of the first obstacles they face just might be the company’s IT department, the panelists suggested.

“IT is process oriented,” Mottl says. The IT team “knows how to keep data secure, to set up servers. But when you bring in a data science team, they want sandboxes, they want freedom, they want to explore and play.”

Also, Nvidia’s Johnson pointed out, “There is a language barrier.” The AI world, he says, is very different from networking or storage, and data scientists find it hard to articulate their requirements to IT.

On the ground or in the cloud?

And then there is the decision of where exactly machine learning should happen—on site, or in the cloud? At Lazard, Mottl says, the deep learning engineers do their experimentation on premises; that’s their sandbox. “But when we deploy, we deploy in the cloud,” he says.

Nvidia, Johnson says, thinks the opposite approach is better. We see the cloud as “the sandbox,” he says. “So you can run as many experiments as possible, fail fast, and learn faster.”

For Cisco’s Irving, the “where” of machine learning depends on the confidentiality of the data.

Mottl, who says rolling machine learning technology into operation can hit resistance from all across the company, had one last word of caution for those aiming to implement AI:

Data scientists are building things that might change the ways other people in the organization work, like sales and even knowledge workers. [You need to] think about the internal stakeholders and prepare them, because the last thing you want to do is to create a valuable new thing that nobody likes and people take potshots against.

The AI Symposium was organized by the Silicon Valley chapters of the IEEE Young Professionals, the IEEE Consultants’ Network, and IEEE Women in Engineering and supported by Cisco. Continue reading

Posted in Human Robots

#436005 NASA Hiring Engineers to Develop “Next ...

It’s been nearly six years since NASA unveiled Valkyrie, a state-of-the-art full-size humanoid robot. After the DARPA Robotics Challenge, NASA has continued to work with Valkyrie at Johnson Space Center, and has also provided Valkyrie robots to several different universities. Although it’s not a new platform anymore (six years is a long time in robotics), Valkyrie is still very capable, with plenty of potential for robotics research.

With that in mind, we were caught by surprise when over the last several months, Jacobs, a Dallas-based engineering company that appears to provide a wide variety of technical services to anyone who wants them, has posted several open jobs in need of roboticists in the Houston, Texas, area who are interested in working with NASA on “the next generation of humanoid robot.”

Here are the relevant bullet points from the one of the job descriptions (which you can view at this link):

Work directly with NASA Johnson Space Center in designing the next generation of humanoid robot.

Join the Valkyrie humanoid robot team in NASA’s Robotic Systems Technology Branch.

Build on the success of the existing Valkyrie and Robonaut 2 humanoid robots and advance NASA’s ability to project a remote human presence and dexterous manipulation capability into challenging, dangerous, and distant environments both in space and here on earth.

The question is, why is NASA developing its own humanoid robot (again) when it could instead save a whole bunch of time and money by using a platform that already exists, whether it’s Atlas, Digit, Valkyrie itself, or one of the small handful of other humanoids that are more or less available? The only answer that I can come up with is that no existing platforms meet NASA’s requirements, whatever those may be. And if that’s the case, what kind of requirements are we talking about? The obvious one would be the ability to work in the kinds of environments that NASA specializes in—space, the Moon, and Mars.

Image: NASA

Artist’s concept of NASA’s Valkyrie humanoid robot working on the surface of Mars.

NASA’s existing humanoid robots, including Robonaut 2 and Valkyrie, were designed to operate on Earth. Robonaut 2 ended up going to space anyway (it’s recently returned to Earth for repairs), but its hardware was certainly never intended to function outside of the International Space Station. Working in a vacuum involves designing for a much more rigorous set of environmental challenges, and things get even worse on the Moon or on Mars, where highly abrasive dust gets everywhere.

We know that it’s possible to design robots for long term operation in these kinds of environments because we’ve done it before. But if you’re not actually going to send your robot off-world, there’s very little reason to bother making sure that it can operate through (say) 300° Celsius temperature swings like you’d find on the Moon. In the past, NASA has quite sensibly focused on designing robots that can be used as platforms for the development of software and techniques that could one day be applied to off-world operations, without over-engineering those specific robots to operate in places that they would almost certainly never go. As NASA increasingly focuses on a return to the Moon, though, maybe it’s time to start thinking about a humanoid robot that could actually do useful stuff on the lunar surface.

Image: NASA

Artist’s concept of the Gateway moon-orbiting space station (seen on the right) with an Orion crew vehicle approaching.

The other possibility that I can think of, and perhaps the more likely one, is that this next humanoid robot will be a direct successor to Robonaut 2, intended for NASA’s Gateway space station orbiting the Moon. Some of the robotics folks at NASA that we’ve talked to recently have emphasized how important robotics will be for Gateway:

Trey Smith, NASA Ames: Everybody at NASA is really excited about work on the Gateway space station that would be in near lunar space. We don’t have definite plans for what would happen on the Gateway yet, but there’s a general recognition that intra-vehicular robots are important for space stations. And so, it would not be surprising to see a mobile manipulator like Robonaut, and a free flyer like Astrobee, on the Gateway.

If you have an un-crewed cargo vehicle that shows up stuffed to the rafters with cargo bags and it docks with the Gateway when there’s no crew there, it would be very useful to have intra-vehicular robots that can pull all those cargo bags out, unpack them, stow all the items, and then even allow the cargo vehicle to detach before the crew show up so that the crew don’t have to waste their time with that.

Julia Badger, NASA JSC: One of the systems on board Gateway is going to be intravehicular robots. They’re not going to necessarily look like Robonaut, but they’ll have some of the same functionality as Robonaut—being mobile, being able to carry payloads from one part of the module to another, doing some dexterous manipulation tasks, inspecting behind panels, those sorts of things.

Image: NASA

Artist’s concept of NASA’s Valkyrie humanoid robot working inside a spacecraft.

Since Gateway won’t be crewed by humans all of the time, it’ll be important to have a permanent robotic presence to keep things running while nobody is home while saving on resources by virtue of the fact that robots aren’t always eating food, drinking water, consuming oxygen, demanding that the temperature stays just so, and producing a variety of disgusting kinds of waste. Obviously, the robot won’t be as capable as humans, but if they can manage to do even basic continuing maintenance tasks (most likely through at least partial teleoperation), that would be very useful.

Photo: Evan Ackerman/IEEE Spectrum

NASA’s Robonaut team plans to perform a variety of mobility and motion-planning experiments using the robot’s new legs, which can grab handrails on the International Space Station.

As for whether robots designed for Gateway would really fall into the “humanoid” category, it’s worth considering that Gateway is designed for humans, implying that an effective robotic system on Gateway would need to be able to interact with the station in similar ways to how a human astronaut would. So, you’d expect to see arms with end-effectors that can grip things as well as push buttons, and some kind of mobility system—the legged version of Robonaut 2 seems like a likely template, but redesigned from the ground up to work in space, incorporating all the advances in robotics hardware and computing that have taken place over the last decade.

We’ve been pestering NASA about this for a little bit now, and they’re not ready to comment on this project, or even to confirm it. And again, everything in this article (besides the job post, which you should totally check out and consider applying for) is just speculation on our part, and we could be wrong about absolutely all of it. As soon as we hear more, we’ll definitely let you know. Continue reading

Posted in Human Robots

#435828 Video Friday: Boston Dynamics’ ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

RoboBusiness 2019 – October 1-3, 2019 – Santa Clara, Calif., USA
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.

You’ve almost certainly seen the new Spot and Atlas videos from Boston Dynamics, if for no other reason than we posted about Spot’s commercial availability earlier this week. But what, are we supposed to NOT include them in Video Friday anyway? Psh! Here you go:

[ Boston Dynamics ]

Eight deadly-looking robots. One Giant Nut trophy. Tonight is the BattleBots season finale, airing on Discovery, 8 p.m. ET, or check your local channels.

[ BattleBots ]

Thanks Trey!

Speaking of battling robots… Having giant robots fight each other is one of those things that sounds really great in theory, but doesn’t work out so well in reality. And sadly, MegaBots is having to deal with reality, which means putting their giant fighting robot up on eBay.

As of Friday afternoon, the current bid is just over $100,000 with a week to go.

[ MegaBots ]

Michigan Engineering has figured out the secret formula to getting 150,000 views on YouTube: drone plus nail gun.

[ Michigan Engineering ]

Michael Burke from the University of Edinburgh writes:

We’ve been learning to scoop grapefruit segments using a PR2, by “feeling” the difference between peel and pulp. We use joint torque measurements to predict the probability that the knife is in the peel or pulp, and use this to apply feedback control to a nominal cutting trajectory learned from human demonstration, so that we remain in a position of maximum uncertainty about which medium we’re cutting. This means we slice along the boundary between the two mediums. It works pretty well!

[ Paper ] via [ Robust Autonomy and Decisions Group ]

Thanks Michael!

Hey look, it’s Jan with eight EMYS robot heads. Hi, Jan! Hi, EMYSes!

[ EMYS ]

We’re putting the KRAKEN Arm through its paces, demonstrating that it can unfold from an Express Rack locker on the International Space Station and access neighboring lockers in NASA’s FabLab system to enable transfer of materials and parts between manufacturing, inspection, and storage stations. The KRAKEN arm will be able to change between multiple ’end effector’ tools such as grippers and inspection sensors – those are in development so they’re not shown in this video.

[ Tethers Unlimited ]

UBTECH’s Alpha Mini Robot with Smart Robot’s “Maatje” software is offering healthcare service to children at Praktijk Intraverte Multidisciplinary Institution in Netherlands.

This institution is using Alpha Mini in counseling children’s behavior. Alpha Mini can move and talk to children and offers games and activities to stimulate and interact with them. Alpha Mini talks, helps and motivates children thereby becoming more flexible in society.

[ UBTECH ]

Some impressive work here from Anusha Nagabandi, Kurt Konoglie, Sergey Levine, Vikash Kumar at Google Brain, training a dexterous multi-fingered hand to do that thing with two balls that I’m really bad at.

Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also notoriously difficult to control: Performing in-hand object manipulation, executing finger gaits to move objects, and exhibiting precise fine motor skills such as writing, all require finely balancing contact forces, breaking and reestablishing contacts repeatedly, and maintaining control of unactuated objects. In this work, we demonstrate that our method of online planning with deep dynamics models (PDDM) addresses both of these limitations; we show that improvements in learned dynamics models, together with improvements in online model-predictive control, can indeed enable efficient and effective learning of flexible contact-rich dexterous manipulation skills — and that too, on a 24-DoF anthropomorphic hand in the real world, using just 2-4 hours of purely real-world data to learn to simultaneously coordinate multiple free-floating objects.

[ PDDM ]

Thanks Vikash!

CMU’s Ballbot has a deceptively light touch that’s ideal for leading people around.

A paper on this has been submitted to IROS 2019.

[ CMU ]

The Autonomous Robots Lab at the University of Nevada is sharing some of the work they’ve done on path planning and exploration for aerial robots during the DARPA SubT Challenge.

[ Autonomous Robots Lab ]

More proof that anything can be a drone if you staple some motors to it. Even 32 feet of styrofoam insulation.

[ YouTube ]

Whatever you think of military drones, we can all agree that they look cool.

[ Boeing ]

I appreciate the fact that iCub has eyelids, I really do, but sometimes, it ends up looking kinda sleepy in research videos.

[ EPFL LASA ]

Video shows autonomous flight of a lightweight aerial vehicle outdoors and indoors on the campus of Carnegie Mellon University. The vehicle is equipped with limited onboard sensing from a front-facing camera and a proximity sensor. The aerial autonomy is enabled by utilizing a 3D prior map built in Step 1.

[ CMU ]

The Stanford Space Robotics Facility allows researchers to test innovative guidance and navigation algorithms on a realistic frictionless, underactuated system.

[ Stanford ASL ]

In this video, Ian and CP discuss Misty’s many capabilities including robust locomotion, obstacle avoidance, 3D mapping/SLAM, face detection and recognition, sound localization, hardware extensibility, photo and video capture, and programmable personality. They also talk about some of the skills he’s built using these capabilities (and others) and how those skills can be expanded upon by you.

[ Misty Robotics ]

This week’s CMU RI Seminar comes from Aaron Parness at Caltech and NASA JPL, on “Robotic Grippers for Planetary Applications.”

The previous generation of NASA missions to the outer solar system discovered salt water oceans on Europa and Enceladus, each with more liquid water than Earth – compelling targets to look for extraterrestrial life. Closer to home, JAXA and NASA have imaged sky-light entrances to lava tube caves on the Moon more than 100 m in diameter and ESA has characterized the incredibly varied and complex terrain of Comet 67P. While JPL has successfully landed and operated four rovers on the surface of Mars using a 6-wheeled rocker-bogie architecture, future missions will require new mobility architectures for these extreme environments. Unfortunately, the highest value science targets often lie in the terrain that is hardest to access. This talk will explore robotic grippers that enable missions to these extreme terrains through their ability to grip a wide variety of surfaces (shapes, sizes, and geotechnical properties). To prepare for use in space where repair or replacement is not possible, we field-test these grippers and robots in analog extreme terrain on Earth. Many of these systems are enabled by advances in autonomy. The talk will present a rapid overview of my work and a detailed case study of an underactuated rock gripper for deflecting asteroids.

[ CMU ]

Rod Brooks gives some of the best robotics talks ever. He gave this one earlier this week at UC Berkeley, on “Steps Toward Super Intelligence and the Search for a New Path.”

[ UC Berkeley ] Continue reading

Posted in Human Robots

#435816 This Light-based Nervous System Helps ...

Last night, way past midnight, I stumbled onto my porch blindly grasping for my keys after a hellish day of international travel. Lights were low, I was half-asleep, yet my hand grabbed the keychain, found the lock, and opened the door.

If you’re rolling your eyes—yeah, it’s not exactly an epic feat for a human. Thanks to the intricate wiring between our brain and millions of sensors dotted on—and inside—our skin, we know exactly where our hand is in space and what it’s touching without needing visual confirmation. But this combined sense of the internal and the external is completely lost to robots, which generally rely on computer vision or surface mechanosensors to track their movements and their interaction with the outside world. It’s not always a winning strategy.

What if, instead, we could give robots an artificial nervous system?

This month, a team led by Dr. Rob Shepard at Cornell University did just that, with a seriously clever twist. Rather than mimicking the electric signals in our nervous system, his team turned to light. By embedding optical fibers inside a 3D printed stretchable material, the team engineered an “optical lace” that can detect changes in pressure less than a fraction of a pound, and pinpoint the location to a spot half the width of a tiny needle.

The invention isn’t just an artificial skin. Instead, the delicate fibers can be distributed both inside a robot and on its surface, giving it both a sense of tactile touch and—most importantly—an idea of its own body position in space. Optical lace isn’t a superficial coating of mechanical sensors; it’s an entire platform that may finally endow robots with nerve-like networks throughout the body.

Eventually, engineers hope to use this fleshy, washable material to coat the sharp, cold metal interior of current robots, transforming C-3PO more into the human-like hosts of Westworld. Robots with a “bodily” sense could act as better caretakers for the elderly, said Shepard, because they can assist fragile people without inadvertently bruising or otherwise harming them. The results were published in Science Robotics.

An Unconventional Marriage
The optical lace is especially creative because it marries two contrasting ideas: one biological-inspired, the other wholly alien.

The overarching idea for optical lace is based on the animal kingdom. Through sight, hearing, smell, taste, touch, and other senses, we’re able to interpret the outside world—something scientists call exteroception. Thanks to our nervous system, we perform these computations subconsciously, allowing us to constantly “perceive” what’s going on around us.

Our other perception is purely internal. Proprioception (sorry, it’s not called “inception” though it should be) is how we know where our body parts are in space without having to look at them, which lets us perform complex tasks when blind. Although less intuitive than exteroception, proprioception also relies on stretching and other deformations within the muscles and tendons and receptors under the skin, which generate electrical currents that shoot up into the brain for further interpretation.

In other words, in theory it’s possible to recreate both perceptions with a single information-carrying system.

Here’s where the alien factor comes in. Rather than using electrical properties, the team turned to light as their data carrier. They had good reason. “Compared with electricity, light carries information faster and with higher data densities,” the team explained. Light can also transmit in multiple directions simultaneously, and is less susceptible to electromagnetic interference. Although optical nervous systems don’t exist in the biological world, the team decided to improve on Mother Nature and give it a shot.

Optical Lace
The construction starts with engineering a “sheath” for the optical nerve fibers. The team first used an elastic polyurethane—a synthetic material used in foam cushioning, for example—to make a lattice structure filled with large pores, somewhat like a lattice pie crust. Thanks to rapid, high-resolution 3D printing, the scaffold can have different stiffness from top to bottom. To increase sensitivity to the outside world, the team made the top of the lattice soft and pliable, to better transfer force to mechanical sensors. In contrast, the “deeper” regions held their structure better, and kept their structure under pressure.

Now the fun part. The team next threaded stretchable “light guides” into the scaffold. These fibers transmit photons, and are illuminated with a blue LED light. One, the input light guide, ran horizontally across the soft top part of the scaffold. Others ran perpendicular to the input in a “U” shape, going from more surface regions to deeper ones. These are the output guides. The architecture loosely resembles the wiring in our skin and flesh.

Normally, the output guides are separated from the input by a small air gap. When pressed down, the input light fiber distorts slightly, and if the pressure is high enough, it contacts one of the output guides. This causes light from the input fiber to “leak” to the output one, so that it lights up—the stronger the pressure, the brighter the output.

“When the structure deforms, you have contact between the input line and the output lines, and the light jumps into these output loops in the structure, so you can tell where the contact is happening,” said study author Patricia Xu. “The intensity of this determines the intensity of the deformation itself.”

Double Perception
As a proof-of-concept for proprioception, the team made a cylindrical lace with one input and 12 output channels. They varied the stiffness of the scaffold along the cylinder, and by pressing down at different points, were able to calculate how much each part stretched and deformed—a prominent precursor to knowing where different regions of the structure are moving in space. It’s a very rudimentary sort of proprioception, but one that will become more sophisticated with increasing numbers of strategically-placed mechanosensors.

The test for exteroception was a whole lot stranger. Here, the team engineered another optical lace with 15 output channels and turned it into a squishy piano. When pressed down, an Arduino microcontroller translated light output signals into sound based on the position of each touch. The stronger the pressure, the louder the volume. While not a musical masterpiece, the demo proved their point: the optical lace faithfully reported the strength and location of each touch.

A More Efficient Robot
Although remarkably novel, the optical lace isn’t yet ready for prime time. One problem is scalability: because of light loss, the material is limited to a certain size. However, rather than coating an entire robot, it may help to add optical lace to body parts where perception is critical—for example, fingertips and hands.

The team sees plenty of potential to keep developing the artificial flesh. Depending on particular needs, both the light guides and scaffold can be modified for sensitivity, spatial resolution, and accuracy. Multiple optical fibers that measure for different aspects—pressure, pain, temperature—can potentially be embedded in the same region, giving robots a multitude of senses.

In this way, we hope to reduce the number of electronics and combine signals from multiple sensors without losing information, the authors said. By taking inspiration from biological networks, it may even be possible to use various inputs through an optical lace to control how the robot behaves, closing the loop from sensation to action.

Image Credit: Cornell Organic Robotics Lab. A flexible, porous lattice structure is threaded with stretchable optical fibers containing more than a dozen mechanosensors and attached to an LED light. When the lattice structure is pressed, the sensors pinpoint changes in the photon flow. Continue reading

Posted in Human Robots