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#436202 Trump CTO Addresses AI, Facial ...

Michael Kratsios, the Chief Technology Officer of the United States, took the stage at Stanford University last week to field questions from Stanford’s Eileen Donahoe and attendees at the 2019 Fall Conference of the Institute for Human-Centered Artificial Intelligence (HAI).

Kratsios, the fourth to hold the U.S. CTO position since its creation by President Barack Obama in 2009, was confirmed in August as President Donald Trump’s first CTO. Before joining the Trump administration, he was chief of staff at investment firm Thiel Capital and chief financial officer of hedge fund Clarium Capital. Donahoe is Executive Director of Stanford’s Global Digital Policy Incubator and served as the first U.S. Ambassador to the United Nations Human Rights Council during the Obama Administration.

The conversation jumped around, hitting on both accomplishments and controversies. Kratsios touted the administration’s success in fixing policy around the use of drones, its memorandum on STEM education, and an increase in funding for basic research in AI—though the magnitude of that increase wasn’t specified. He pointed out that the Trump administration’s AI policy has been a continuation of the policies of the Obama administration, and will continue to build on that foundation. As proof of this, he pointed to Trump’s signing of the American AI Initiative earlier this year. That executive order, Kratsios said, was intended to bring various government agencies together to coordinate their AI efforts and to push the idea that AI is a tool for the American worker. The AI Initiative, he noted, also took into consideration that AI will cause job displacement, and asked private companies to pledge to retrain workers.

The administration, he said, is also looking to remove barriers to AI innovation. In service of that goal, the government will, in the next month or so, release a regulatory guidance memo instructing government agencies about “how they should think about AI technologies,” said Kratsios.

U.S. vs China in AI

A few of the exchanges between Kratsios and Donahoe hit on current hot topics, starting with the tension between the U.S. and China.

Donahoe:

“You talk a lot about unique U.S. ecosystem. In which aspect of AI is the U.S. dominant, and where is China challenging us in dominance?

Kratsios:

“They are challenging us on machine vision. They have more data to work with, given that they have surveillance data.”

Donahoe:

“To what extent would you say the quantity of data collected and available will be a determining factor in AI dominance?”

Kratsios:

“It makes a big difference in the short term. But we do research on how we get over these data humps. There is a future where you don’t need as much data, a lot of federal grants are going to [research in] how you can train models using less data.”

Donahoe turned the conversation to a different tension—that between innovation and values.

Donahoe:

“A lot of conversation yesterday was about the tension between innovation and values, and how do you hold those things together and lead in both realms.”

Kratsios:

“We recognized that the U.S. hadn’t signed on to principles around developing AI. In May, we signed [the Organization for Economic Cooperation and Development Principles on Artificial Intelligence], coming together with other Western democracies to say that these are values that we hold dear.

[Meanwhile,] we have adversaries around the world using AI to surveil people, to suppress human rights. That is why American leadership is so critical: We want to come out with the next great product. And we want our values to underpin the use cases.”

A member of the audience pushed further:

“Maintaining U.S. leadership in AI might have costs in terms of individuals and society. What costs should individuals and society bear to maintain leadership?”

Kratsios:

“I don’t view the world that way. Our companies big and small do not hesitate to talk about the values that underpin their technology. [That is] markedly different from the way our adversaries think. The alternatives are so dire [that we] need to push efforts to bake the values that we hold dear into this technology.”

Facial recognition

And then the conversation turned to the use of AI for facial recognition, an application which (at least for police and other government agencies) was recently banned in San Francisco.

Donahoe:

“Some private sector companies have called for government regulation of facial recognition, and there already are some instances of local governments regulating it. Do you expect federal regulation of facial recognition anytime soon? If not, what ought the parameters be?”

Kratsios:

“A patchwork of regulation of technology is not beneficial for the country. We want to avoid that. Facial recognition has important roles—for example, finding lost or displaced children. There are use cases, but they need to be underpinned by values.”

A member of the audience followed up on that topic, referring to some data presented earlier at the HAI conference on bias in AI:

“Frequently the example of finding missing children is given as the example of why we should not restrict use of facial recognition. But we saw Joy Buolamwini’s presentation on bias in data. I would like to hear your thoughts about how government thinks we should use facial recognition, knowing about this bias.”

Kratsios:

“Fairness, accountability, and robustness are things we want to bake into any technology—not just facial recognition—as we build rules governing use cases.”

Immigration and innovation

A member of the audience brought up the issue of immigration:

“One major pillar of innovation is immigration, does your office advocate for it?”

Kratsios:

“Our office pushes for best and brightest people from around the world to come to work here and study here. There are a few efforts we have made to move towards a more merit-based immigration system, without congressional action. [For example, in] the H1-B visa system, you go through two lotteries. We switched the order of them in order to get more people with advanced degrees through.”

The government’s tech infrastructure

Donahoe brought the conversation around to the tech infrastructure of the government itself:

“We talk about the shiny object, AI, but the 80 percent is the unsexy stuff, at federal and state levels. We don’t have a modern digital infrastructure to enable all the services—like a research cloud. How do we create this digital infrastructure?”

Kratsios:

“I couldn’t agree more; the least partisan issue in Washington is about modernizing IT infrastructure. We spend like $85 billion a year on IT at the federal level, we can certainly do a better job of using those dollars.” Continue reading

Posted in Human Robots

#436100 Labrador Systems Developing Affordable ...

Developing robots for the home is still a challenge, especially if you want those robots to interact with people and help them do practical, useful things. However, the potential markets for home robots are huge, and one of the most compelling markets is for home robots that can assist humans who need them. Today, Labrador Systems, a startup based in California, is announcing a pre-seed funding round of $2 million (led by SOSV’s hardware accelerator HAX with participation from Amazon’s Alexa Fund and iRobot Ventures, among others) with the goal of expanding development and conducting pilot studies of “a new [assistive robot] platform for supporting home health.”

Labrador was founded two years ago by Mike Dooley and Nikolai Romanov. Both Mike and Nikolai have backgrounds in consumer robotics at Evolution Robotics and iRobot, but as an ’80s gamer, Mike’s bio (or at least the parts of his bio on LinkedIn) caught my attention: From 1995 to 1997, Mike worked at Brøderbund Software, helping to manage play testing for games like Myst and Riven and the Where in the World is Carmen San Diego series. He then spent three years at Lego as the product manager for MindStorms. After doing some marginally less interesting things, Mike was the VP of product development at Evolution Robotics from 2006 to 2012, where he led the team that developed the Mint floor sweeping robot. Evolution was acquired by iRobot in 2012, and Mike ended up as the VP of product development over there until 2017, when he co-founded Labrador.

I was pretty much sold at Where in the World is Carmen San Diego (the original version of which I played from a 5.25” floppy on my dad’s Apple IIe)*, but as you can see from all that other stuff, Mike knows what he’s doing in robotics as well.

And according to Labrador’s press release, what they’re doing is this:

Labrador Systems is an early stage technology company developing a new generation of assistive robots to help people live more independently. The company’s core focus is creating affordable solutions that address practical and physical needs at a fraction of the cost of commercial robots. … Labrador’s technology platform offers an affordable solution to improve the quality of care while promoting independence and successful aging.

Labrador’s personal robot, the company’s first offering, will enter pilot studies in 2020.

That’s about as light on detail as a press release gets, but there’s a bit more on Labrador’s website, including:

Our core focus is creating affordable solutions that address practical and physical needs. (we are not a social robot company)
By affordable, we mean products and technologies that will be available at less than 1/10th the cost of commercial robots.
We achieve those low costs by fusing the latest technologies coming out of augmented reality with robotics to move things in the real world.

The only hardware we’ve actually seen from Labrador at this point is a demo that they put together for Amazon’s re:MARS conference, which took place a few months ago, showing a “demonstration project” called Smart Walker:

This isn’t the home assistance robot that Labrador got its funding for, but rather a demonstration of some of their technology. So of course, the question is, what’s Labrador working on, then? It’s still a secret, but Mike Dooley was able to give us a few more details.

IEEE Spectrum: Your website shows a smart walker concept—how is that related to the assistive robot that you’re working on?

Mike Dooley: The smart walker was a request from a major senior living organization to have our robot (which is really good at navigation) guide residents from place to place within their communities. To test the idea with residents, it turned out to be much quicker to take the navigation system from the robot and put it on an existing rollator walker. So when you see the clips of the technology in the smart walker video on our website, that’s actually the robot’s navigation system localizing in real time and path planning in an environment.

“Assistive robot” can cover a huge range of designs and capabilities—can you give us any more detail about your robot, and what it’ll be able to do?

One of the core features of our robot is to help people move things where they have difficulty moving themselves, particularly in the home setting. That may sound trivial, but to someone who has impaired mobility, it can be a major daily challenge and negatively impact their life and health in a number of ways. Some examples we repeatedly hear are people not staying hydrated or taking their medication on time simply because there is a distance between where they are and the items they need. Once we have those base capabilities, i.e. the ability to navigate around a home and move things within it, then the robot becomes a platform for a wider variety of applications.

What made you decide to develop assistive robots, and why are robots a good solution for seniors who want to live independently?

Supporting independent living has been seen as a massive opportunity in robotics for some time, but also as something off in the future. The turning point for me was watching my mother enter that stage in her life and seeing her transition to using a cane, then a walker, and eventually to a wheelchair. That made the problems very real for me. It also made things much clearer about how we could start addressing specific needs with the tools that are becoming available now.

In terms of why robots can be a good solution, the basic answer is the level of need is so overwhelming that even helping with “basic” tasks can make an appreciable difference in the quality of someone’s daily life. It’s also very much about giving individuals a degree of control back over their environment. That applies to seniors as well as others whose world starts getting more complex to manage as their abilities become more impaired.

What are the particular challenges of developing assistive robots, and how are you addressing them? Why do you think there aren’t more robotics startups in this space?

The setting (operating in homes and personal spaces) and the core purpose of the product (aiding a wide variety of individuals) bring a lot of complexity to any capability you want to build into an assistive robot. Our approach is to put as much structure as we can into the system to make it functional, affordable, understandable and reliable.

I think one of the reasons you don’t see more startups in the space is that a lot of roboticists want to skip ahead and do the fancy stuff, such as taking on human-level capabilities around things like manipulation. Those are very interesting research topics, but we think those are also very far away from being practical solutions you can productize for people to use in their homes.

How do you think assistive robots and human caregivers should work together?

The ideal scenario is allowing caregivers to focus more of their time on the high-touch, personal side of care. The robot can offload the more basic support tasks as well as extend the impact of the caregiver for the long hours of the day they can’t be with someone at their home. We see that applying to both paid care providers as well as the 40 million unpaid family members and friends that provide assistance.

The robot is really there as a tool, both for individuals in need and the people that help them. What’s promising in the research discussions we’ve had so far, is that even when a caregiver is present, giving control back to the individual for simple things can mean a lot in the relationship between them and the caregiver.

What should we look forward to from Labrador in 2020?

Our big goal in 2020 is to start placing the next version of the robot with individuals with different types of needs to let them experience it naturally in their own homes and provide feedback on what they like, what don’t like and how we can make it better. We are currently reaching out to companies in the healthcare and home health fields to participate in those studies and test specific applications related to their services. We plan to share more detail about those studies and the robot itself as we get further into 2020.

If you’re an organization (or individual) who wants to possibly try out Labrador’s prototype, the company encourages you to connect with them through their website. And as we learn more about what Labrador is up to, we’ll have updates for you, presumably in 2020.

[ Labrador Systems ]

* I just lost an hour of my life after finding out that you can play Where in the World is Carmen San Diego in your browser for free. Continue reading

Posted in Human Robots

#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

#436042 Video Friday: Caltech’s Drone With ...

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!):

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.

Caltech has been making progress on LEONARDO (LEg ON Aerial Robotic DrOne), their leggy thruster powered humanoid-thing. It can now balance and walk, which is quite impressive to see.

We’ll circle back again when they’ve got it jumping and floating around.

[ Caltech ]

Turn the subtitles on to learn how robots became experts at slicing bubbly, melty, delicious cheese.

These robots learned how to do the traditional Swiss raclette from demonstration. The Robot Learning & Interaction group at the Idiap Research Institute has developed an imitation learning technique allowing the robot to acquire new skills by considering position and force information, with an automatic adaptation to new situations. The range of applications is wide, including industrial robots, service robots, and assistive robots.

[ Idiap ]

Thanks Sylvain!

Some amazing news this week from Skydio, with the announcement of their better in every single way Skydio 2 autonomous drone. Read our full article for details, but here’s a getting started video that gives you an overview of what the drone can do.

The first batch sold out in 36 hours, but you can put down a $100 deposit to reserve the $999 drone for 2020 delivery.

[ Skydio ]

UBTECH is introducing a couple new robot kits for the holidays: ChampBot and FireBot.

$130 each, available on October 20.

[ Ubtech ]

NASA’s InSight lander on Mars is trying to use its robotic arm to get the mission’s heat flow probe, or mole, digging again. InSight team engineer Ashitey Trebbi-Ollennu, based at NASA’s Jet Propulsion Laboratory in Pasadena, California, explains what has been attempted and the game plan for the coming weeks. The next tactic they’ll try will be “pinning” the mole against the hole it’s in.

[ NASA ]

We introduce shape-changing swarm robots. A swarm of self-transformable robots can both individually and collectively change their configuration to display information, actuate objects, act as tangible controllers, visualize data, and provide physical affordances. ShapeBots is a concept prototype of shape-changing swarm robots. Each robot can change its shape by leveraging small linear actuators that are thin (2.5 cm) and highly extendable (up to 20cm) in both horizontal and vertical directions.

[ Ryo Suzuki ]

Robot abuse!

Vision 60 legged robot managing unstructured terrain without vision or force sensors in its legs. Using only high-transparency actuators and 2kHz algorithmic stability control… 4-limbs and 12-motors with only a velocity command.

[ Ghost Robotics ]

We asked real people to bring in real products they needed picked for their application. In MINUTES, we assembled the right tool.

This is a cool idea, but for a real challenge they should try it outside a supermarket. Or a pet store.

[ Soft Robotics ]

Good water quality is important to humans and to nature. In a country with as much water as the Netherlands has, ensuring water quality is a very labour-intensive undertaking. To address this issue, researchers from TU Delft have developed a ‘pelican drone’: a drone capable of taking water samples quickly, in combination with a measuring instrument that immediately analyses the water quality. The drone was tested this week at the new Marker Wadden nature area ‘Living Lab’.

[ MAVLab ]

In an international collaboration led by scientists in Switzerland, three amputees merge with their bionic prosthetic legs as they climb over various obstacles without having to look. The amputees report using and feeling their bionic leg as part of their own body, thanks to sensory feedback from the prosthetic leg that is delivered to nerves in the leg’s stump.

[ EPFL ]

It’s a little hard to see, but this is one way of testing out asteroid imaging spacecraft without actually going into space: a fake asteroid and a 2D microgravity simulator.

[ Caltech ]

Drones can help filmmakers do the kinds of shots that would be otherwise impossible.

[ DJI ]

Two long interviews this week from Lex Fridman’s AI Podcast, and both of them are worth watching: Gary Marcus, and Peter Norvig.

[ AI Podcast ]

This week’s CMU RI Seminar comes from Tucker Hermans at the University of Utah, on “Improving Multi-fingered Robot Manipulation by Unifying Learning and Planning.”

Multi-fingered hands offer autonomous robots increased dexterity, versatility, and stability over simple two-fingered grippers. Naturally, this increased ability comes with increased complexity in planning and executing manipulation actions. As such, I propose combining model-based planning with learned components to improve over purely data-driven or purely-model based approaches to manipulation. This talk examines multi-fingered autonomous manipulation when the robot has only partial knowledge of the object of interest. I will first present results on planning multi-fingered grasps for novel objects using a learned neural network. I will then present our approach to planning in-hand manipulation tasks when dynamic properties of objects are not known. I will conclude with a discussion of our ongoing and future research to further unify these two approaches.

[ CMU RI ] Continue reading

Posted in Human Robots

#435822 The Internet Is Coming to the Rest of ...

People surf it. Spiders crawl it. Gophers navigate it.

Now, a leading group of cognitive biologists and computer scientists want to make the tools of the Internet accessible to the rest of the animal kingdom.

Dubbed the Interspecies Internet, the project aims to provide intelligent animals such as elephants, dolphins, magpies, and great apes with a means to communicate among each other and with people online.

And through artificial intelligence, virtual reality, and other digital technologies, researchers hope to crack the code of all the chirps, yips, growls, and whistles that underpin animal communication.

Oh, and musician Peter Gabriel is involved.

“We can use data analysis and technology tools to give non-humans a lot more choice and control,” the former Genesis frontman, dressed in his signature Nehru-style collar shirt and loose, open waistcoat, told IEEE Spectrum at the inaugural Interspecies Internet Workshop, held Monday in Cambridge, Mass. “This will be integral to changing our relationship with the natural world.”

The workshop was a long time in the making.

Eighteen years ago, Gabriel visited a primate research center in Atlanta, Georgia, where he jammed with two bonobos, a male named Kanzi and his half-sister Panbanisha. It was the first time either bonobo had sat at a piano before, and both displayed an exquisite sense of musical timing and melody.

Gabriel seemed to be speaking to the great apes through his synthesizer. It was a shock to the man who once sang “Shock the Monkey.”

“It blew me away,” he says.

Add in the bonobos’ ability to communicate by pointing to abstract symbols, Gabriel notes, and “you’d have to be deaf, dumb, and very blind not to notice language being used.”

Gabriel eventually teamed up with Internet protocol co-inventor Vint Cerf, cognitive psychologist Diana Reiss, and IoT pioneer Neil Gershenfeld to propose building an Interspecies Internet. Presented in a 2013 TED Talk as an “idea in progress,” the concept proved to be ahead of the technology.

“It wasn’t ready,” says Gershenfeld, director of MIT’s Center for Bits and Atoms. “It needed to incubate.”

So, for the past six years, the architects of the Dolittlesque initiative embarked on two small pilot projects, one for dolphins and one for chimpanzees.

At her Hunter College lab in New York City, Reiss developed what she calls the D-Pad—a touchpad for dolphins.

Reiss had been trying for years to create an underwater touchscreen with which to probe the cognition and communication skills of bottlenose dolphins. But “it was a nightmare coming up with something that was dolphin-safe and would work,” she says.

Her first attempt emitted too much heat. A Wii-like system of gesture recognition proved too difficult to install in the dolphin tanks.

Eventually, she joined forces with Rockefeller University biophysicist Marcelo Magnasco and invented an optical detection system in which images and infrared sensors are projected through an underwater viewing window onto a glass panel, allowing the dolphins to play specially designed apps, including one dubbed Whack-a-Fish.

Meanwhile, in the United Kingdom, Gabriel worked with Alison Cronin, director of the ape rescue center Monkey World, to test the feasibility of using FaceTime with chimpanzees.

The chimps engaged with the technology, Cronin reported at this week’s workshop. However, our hominid cousins proved as adept at videotelephonic discourse as my three-year-old son is at video chatting with his grandparents—which is to say, there was a lot of pass-the-banana-through-the-screen and other silly games, and not much meaningful conversation.

“We can use data analysis and technology tools to give non-humans a lot more choice and control.”
—Peter Gabriel

The buggy, rudimentary attempt at interspecies online communication—what Cronin calls her “Max Headroom experiment”—shows that building the Interspecies Internet will not be as simple as giving out Skype-enabled tablets to smart animals.

“There are all sorts of problems with creating a human-centered experience for another animal,” says Gabriel Miller, director of research and development at the San Diego Zoo.

Miller has been working on animal-focused sensory tools such as an “Elephone” (for elephants) and a “Joybranch” (for birds), but it’s not easy to design efficient interactive systems for other creatures—and for the Interspecies Internet to be successful, Miller points out, “that will be super-foundational.”

Researchers are making progress on natural language processing of animal tongues. Through a non-profit organization called the Earth Species Project, former Firefox designer Aza Raskin and early Twitter engineer Britt Selvitelle are applying deep learning algorithms developed for unsupervised machine translation of human languages to fashion a Rosetta Stone–like tool capable of interpreting the vocalizations of whales, primates, and other animals.

Inspired by the scientists who first documented the complex sonic arrangements of humpback whales in the 1960s—a discovery that ushered in the modern marine conservation movement—Selvitelle hopes that an AI-powered animal translator can have a similar effect on environmentalism today.

“A lot of shifts happen when someone who doesn’t have a voice gains a voice,” he says.

A challenge with this sort of AI software remains verification and validation. Normally, machine-learning algorithms are benchmarked against a human expert, but who is to say if a cybernetic translation of a sperm whale’s clicks is accurate or not?

One could back-translate an English expression into sperm whale-ese and then into English again. But with the great apes, there might be a better option.

According to primatologist Sue Savage-Rumbaugh, expertly trained bonobos could serve as bilingual interpreters, translating the argot of apes into the parlance of people, and vice versa.

Not just any trained ape will do, though. They have to grow up in a mixed Pan/Homo environment, as Kanzi and Panbanisha were.

“If I can have a chat with a cow, maybe I can have more compassion for it.”
—Jeremy Coller

Those bonobos were raised effectively from birth both by Savage-Rumbaugh, who taught the animals to understand spoken English and to communicate via hundreds of different pictographic “lexigrams,” and a bonobo mother named Matata that had lived for six years in the Congolese rainforests before her capture.

Unlike all other research primates—which are brought into captivity as infants, reared by human caretakers, and have limited exposure to their natural cultures or languages—those apes thus grew up fluent in both bonobo and human.

Panbanisha died in 2012, but Kanzi, aged 38, is still going strong, living at an ape sanctuary in Des Moines, Iowa. Researchers continue to study his cognitive abilities—Francine Dolins, a primatologist at the University of Michigan-Dearborn, is running one study in which Kanzi and other apes hunt rabbits and forage for fruit through avatars on a touchscreen. Kanzi could, in theory, be recruited to check the accuracy of any Google Translate–like app for bonobo hoots, barks, grunts, and cries.

Alternatively, Kanzi could simply provide Internet-based interpreting services for our two species. He’s already proficient at video chatting with humans, notes Emily Walco, a PhD student at Harvard University who has personally Skyped with Kanzi. “He was super into it,” Walco says.

And if wild bonobos in Central Africa can be coaxed to gather around a computer screen, Savage-Rumbaugh is confident Kanzi could communicate with them that way. “It can all be put together,” she says. “We can have an Interspecies Internet.”

“Both the technology and the knowledge had to advance,” Savage-Rumbaugh notes. However, now, “the techniques that we learned could really be extended to a cow or a pig.”

That’s music to the ears of Jeremy Coller, a private equity specialist whose foundation partially funded the Interspecies Internet Workshop. Coller is passionate about animal welfare and has devoted much of his philanthropic efforts toward the goal of ending factory farming.

At the workshop, his foundation announced the creation of the Coller Doolittle Prize, a US $100,000 award to help fund further research related to the Interspecies Internet. (A working group also formed to synthesize plans for the emerging field, to facilitate future event planning, and to guide testing of shared technology platforms.)

Why would a multi-millionaire with no background in digital communication systems or cognitive psychology research want to back the initiative? For Coller, the motivation boils to interspecies empathy.

“If I can have a chat with a cow,” he says, “maybe I can have more compassion for it.”

An abridged version of this post appears in the September 2019 print issue as “Elephants, Dolphins, and Chimps Need the Internet, Too.” Continue reading

Posted in Human Robots