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#437859 We Can Do Better Than Human-Like Hands ...

Posted on December 2, 2020 by Android

One strategy for designing robots that are capable in anthropomorphic environments is to make the robots themselves as anthropomorphic as possible. It makes sense—for example, there are stairs all over the place because humans have legs, and legs are good at stairs, so if we give robots legs like humans, they’ll be good at stairs too, right? We also see this tendency when it comes to robotic grippers, because robots need to grip things that have been optimized for human hands.

Despite some amazing robotic hands inspired by the biology of our own human hands, there are also opportunities for creativity in gripper designs that do things human hands are not physically capable of. At ICRA 2020, researchers from Stanford University presented a paper on the design of a robotic hand that has fingers made of actuated rollers, allowing it to manipulate objects in ways that would tie your fingers into knots.

While it’s got a couple fingers, this prototype “roller grasper” hand tosses anthropomorphic design out the window in favor of unique methods of in-hand manipulation. The roller grasper does share some features with other grippers designed for in-hand manipulation using active surfaces (like conveyor belts embedded in fingers), but what’s new and exciting here is that those articulated active roller fingertips (or whatever non-anthropomorphic name you want to give them) provide active surfaces that are steerable. This means that the hand can grasp objects and rotate them without having to resort to complex sequences of finger repositioning, which is how humans do it.

Photo: Stanford University

Things like picking something flat off of a table, always tricky for robotic hands (and sometimes for human hands as well), is a breeze thanks to the fingertip rollers.

Each of the hand’s fingers has three actuated degrees of freedom, which result in several different ways in which objects can be grasped and manipulated. Things like picking something flat off of a table, always tricky for robotic hands (and sometimes for human hands as well), is a breeze thanks to the fingertip rollers. The motion of an object in this gripper isn’t quite holonomic, meaning that it can’t arbitrarily reorient things without sometimes going through other intermediate steps. And it’s also not compliant in the way that many other grippers are, limiting some types of grasps. This particular design probably won’t replace every gripper out there, but it’s particularly skilled at some specific kinds of manipulations in a way that makes it unique.

We should be clear that it’s not the intent of this paper (or of this article!) to belittle five-fingered robotic hands—the point is that there are lots of things that you can do with totally different hand designs, and just because humans use one kind of hand doesn’t mean that robots need to do the same if they want to match (or exceed) some specific human capabilities. If we could make robotic hands with five fingers that had all of the actuation and sensing and control that our own hands do, that would be amazing, but it’s probably decades away. In the meantime, there are plenty of different designs to explore.

And speaking of exploring different designs, these same folks are already at work on version two of their hand, which replaces the fingertip rollers with fingertip balls:

For more on this new version of the hand (among other things), we spoke with lead author Shenli Yuan via email. And the ICRA page is here if you have questions of your own.

IEEE Spectrum: Human hands are often seen as the standard for manipulation. When adding degrees of freedom that human hands don’t have (as in your work) can make robotic hands more capable than ours in many ways, do you think we should still think of human hands as something to try and emulate?

Shenli Yuan: Yes, definitely. Not only because human hands have great manipulation capability, but because we’re constantly surrounded by objects that were designed and built specifically to be manipulated by the human hand. Anthropomorphic robot hands are still worth investigating, and still have a long way to go before they truly match the dexterity of a human hand. The design we came up with is an exploration of what unique capabilities may be achieved if we are not bound by the constraints of anthropomorphism, and what a biologically impossible mechanism may achieve in robotic manipulation. In addition, for lots of tasks, it isn’t necessarily optimal to try and emulate the human hand. Perhaps in 20 to 50 years when robot manipulators are much better, they won’t look like the human hand that much. The design constraints for robotics and biology have points in common (like mechanical wear, finite tendons stiffness) but also major differences (like continuous rotation for robots and less heat dissipation problems for humans).

“For lots of tasks, it isn’t necessarily optimal to try and emulate the human hand. Perhaps in 20 to 50 years when robot manipulators are much better, they won’t look like the human hand that much.”
—Shenli Yuan, Stanford University

What are some manipulation capabilities of human hands that are the most difficult to replicate with your system?

There are a few things that come to mind. It cannot perform a power grasp (using the whole hand for grasping as opposed to pinch grasp that uses only fingertips), which is something that can be easily done by human hands. It cannot move or rotate objects instantaneously in arbitrary directions or about arbitrary axes, though the human hand is somewhat limited in this respect as well. It also cannot perform gaiting. That being said, these limitations exist largely because this grasper only has 9 degrees of freedom, as opposed to the human hand which has more than 20. We don’t think of this grasper as a replacement for anthropomorphic hands, but rather as a way to provide unique capabilities without all of the complexity associated with a highly actuated, humanlike hand.

What’s the most surprising or impressive thing that your hand is able to do?

The most impressive feature is that it can rotate objects continuously, which is typically difficult or inefficient for humanlike robot hands. Something really surprising was that we put most of our energy into the design and analysis of the grasper, and the control strategy we implemented for demonstrations is very simple. This simple control strategy works surprisingly well with very little tuning or trial-and-error.

With this many degrees of freedom, how complicated is it to get the hand to do what you want it to do?

The number of degrees of freedom is actually not what makes controlling it difficult. Most of the difficulties we encountered were actually due to the rolling contact between the rollers and the object during manipulation. The rolling behavior can be viewed as constantly breaking and re-establishing contacts between the rollers and objects, this very dynamic behavior introduces uncertainties in controlling our grasper. Specifically, it was difficult estimating the velocity of each contact point with the object, which changes based on object and finger position, object shape (especially curvature), and slip/no slip.

What more can you tell us about Roller Grasper V2?

Roller Grasper V2 has spherical rollers, while the V1 has cylindrical rollers. We realized that cylindrical rollers are very good at manipulating objects when the rollers and the object form line contacts, but it can be unstable when the grasp geometry doesn’t allow for a line contact between each roller and the grasped object. Spherical rollers solve that problem by allowing predictable points of contact regardless of how a surface is oriented.

The parallelogram mechanism of Roller Grasper V1 makes the pivot axis offset a bit from the center of the roller, which made our control and analysis more challenging. The kinematics of the Roller Grasper V2 is simpler. The base joint intersects with the finger, which intersects with the pivot joint, and the pivot joint intersects with the roller joint. It’s symmetrical design and simpler kinematics make our control and analysis a lot more straightforward. Roller Grasper V2 also has a larger pivot range of 180 degrees, while V1 is limited to 90 degrees.

In terms of control, we implemented more sophisticated control strategies (including a hand-crafted control strategy and an imitation learning based strategy) for the grasper to perform autonomous in-hand manipulation.

“Design of a Roller-Based Dexterous Hand for Object Grasping and Within-Hand Manipulation,” by Shenli Yuan, Austin D. Epps, Jerome B. Nowak, and J. Kenneth Salisbury from Stanford University is being presented at ICRA 2020.

< Back to IEEE Journal Watch Continue reading →

Posted in Human Robots

#437851 Boston Dynamics’ Spot Robot Dog ...

Posted on December 2, 2020 by Android

Boston Dynamics has been fielding questions about when its robots are going to go on sale and how much they’ll cost for at least a dozen years now. I can say this with confidence, because that’s how long I’ve been a robotics journalist, and I’ve been pestering them about it the entire time. But it’s only relatively recently that the company started to make a concerted push away from developing robots exclusively for the likes of DARPA into platforms with more commercial potential, starting with a compact legged robot called Spot, first introduced in 2016.

Since then, we’ve been following closely as Spot has gone from a research platform to a product, and today, Boston Dynamics is announcing the final step in that process: commercial availability. You can now order a Spot Explorer Kit from the Boston Dynamics online store for US $74,500 (plus tax), shipping included, with delivery in 6 to 8 weeks. FINALLY!

Over the past 10 months or so, Boston Dynamics has leased Spot robots to carefully selected companies, research groups, and even a few individuals as part of their early adopter program—that’s where all of the clips in the video below came from. While there are over 100 Spots out in the world right now, getting one of them has required convincing Boston Dynamics up front that you knew more or less exactly what you wanted to do and how you wanted to do it. If you’re a big construction company or the Jet Propulsion Laboratory or Adam Savage, that’s all well and good, but for other folks who think that a Spot could be useful for them somehow and want to give it a shot, this new availability provides a fewer-strings attached opportunity to do some experimentation with the robot.

There’s a lot of cool stuff going on in that video, but we were told that the one thing that really stood out to the folks at Boston Dynamics was a 2-second clip that you can see on the left-hand side of the screen from 0:19 to 0:21. In it, Spot is somehow managing to walk across a spider web of rebar without getting tripped up, at faster than human speed. This isn’t something that Spot was specifically programmed to do, and in fact the Spot User Guide specifically identifies “rebar mesh” as an unsafe operating environment. But the robot just handles it, and that’s a big part of what makes Spot so useful—its ability to deal with (almost) whatever you can throw at it.

Before you get too excited, Boston Dynamics is fairly explicit that the current license for the robot is intended for commercial use, and the company specifically doesn’t want people to be just using it at home for fun. We know this because we asked (of course we asked), and they told us “we specifically don’t want people to just be using it at home for fun.” Drat. You can still buy one as an individual, but you have to promise that you’ll follow the terms of use and user guidelines, and it sounds like using a robot in your house might be the second-fastest way to invalidate your warranty:

SPOT IS AN AMAZING ROBOT, BUT IS NOT CERTIFIED SAFE FOR IN-HOME USE OR INTENDED FOR USE NEAR CHILDREN OR OTHERS WHO MAY NOT APPRECIATE THE HAZARDS ASSOCIATED WITH ITS OPERATION.

Not being able to get Spot to play with your kids may be disappointing, but for those of you with the sort of kids who are also students, the good news is that Boston Dynamics has carved out a niche for academic institutions, which can buy Spot at a discounted price. And if you want to buy a whole pack of Spots, there’s a bulk discount for Enterprise users as well.

What do you get for $74,500? All this!

Spot robot
Spot battery (2x)
Spot charger
Tablet controller and charger
Robot case for storage and transportation
FREE SHIPPING!

Photo: Boston Dynamics

The basic package includes the robot, two batteries, charger, a tablet controller, and a storage case.

You can view detailed specs here.

So is $75k a lot of money for a robot like Spot, or not all that much? We don’t have many useful points of comparison, partially because it’s not clear to what extent other pre-commercial quadrupedal robots (like ANYmal or Aliengo) share capabilities and features with Spot. For more perspective on Spot’s price tag, we spoke to Michael Perry, vice president of business development at Boston Dynamics.

IEEE Spectrum: Why is Spot so affordable?

Michael Perry: The main goal of selling the robot at this stage is to try to get it into the hands of as many application developers as possible, so that we can learn from the community what the biggest driver of value is for Spot. As a platform, unlocking the value of an ecosystem is our core focus right now.

Spectrum: Why is Spot so expensive?

Perry: Expensive is relative, but compared to the initial prototypes of Spot, we’ve been able to drop down the cost pretty significantly. One key thing has been designing it for robustness—we’ve put hundreds and hundreds of hours on the robot to make sure that it’s able to be successful when it falls, or when it has an electrostatic discharge. We’ve made sure that it’s able to perceive a wide variety of environments that are difficult for traditional vision-based sensors to handle. A lot of that engineering is baked into the core product so that you don’t have to worry about the mobility or robotic side of the equation, you can just focus on application development.

Photos: Boston Dynamics

Accessories for Spot include [clockwise from top left]: Spot GXP with additional ports for payload integration; Spot CAM with panorama camera and advanced comms; Spot CAM+ with pan-tilt-zoom camera for inspections; Spot EAP with lidar to enhance autonomy on large sites; Spot EAP+ with Spot CAM camera plus lidar; and Spot CORE for additional processing power.

The $75k that you’ll pay for the Spot Explorer Kit, it’s important to note, is just the base price for the robot. As with other things that fall into this price range (like a luxury car), there are all kinds of fun ways to drive that cost up with accessories, although for Spot, some of those accessories will be necessary for many (if not most) applications. For example, a couple of expansion ports to make it easier to install your own payloads on Spot will run you $1,275. An additional battery is $4,620. And if you want to really get some work done, the Enhanced Autonomy Package (with 360 cameras, lights, better comms, and a Velodyne VLP-16) will set you back an additional $34,570. If you were hoping for an arm, you’ll have to wait until the end of the year.

Each Spot also includes a year’s worth of software updates and a warranty, although the standard warranty just covers “defects related to materials and workmanship” not “I drove my robot off a cliff” or “I tried to take my robot swimming.” For that sort of thing (user error) to be covered, you’ll need to upgrade to the $12,000 Spot CARE premium service plan to cover your robot for a year as long as you don’t subject it to willful abuse, which both of those examples I just gave probably qualify as.

While we’re on the subject of robot abuse, Boston Dynamics has very sensibly devoted a substantial amount of the Spot User Guide to help new users understand how they should not be using their robot, in order to “lessen the risk of serious injury, death, or robot and other property damage.” According to the guide, some things that could cause Spot to fall include holes, cliffs, slippery surfaces (like ice and wet grass), and cords. Spot’s sensors also get confused by “transparent, mirrored, or very bright obstacles,” and the guide specifically says Spot “may crash into glass doors and windows.” Also this: “Spot cannot predict trajectories of moving objects. Do not operate Spot around moving objects such as vehicles, children, or pets.”

We should emphasize that this is all totally reasonable, and while there are certainly a lot of things to be aware of, it’s frankly astonishing that these are the only things that Boston Dynamics explicitly warns users against. Obviously, not every potentially unsafe situation or thing is described above, but the point is that Boston Dynamics is willing to say to new users, “here’s your robot, go do stuff with it” without feeling the need to hold their hand the entire time.

There’s one more thing to be aware of before you decide to buy a Spot, which is the following:

“All orders will be subject to Boston Dynamics’ Terms and Conditions of Sale which require the beneficial use of its robots.”

Specifically, this appears to mean that you aren’t allowed to (or supposed to) use the robot in a way that could hurt living things, or “as a weapon, or to enable any weapon.” The conditions of sale also prohibit using the robot for “any illegal or ultra-hazardous purpose,” and there’s some stuff in there about it not being cool to use Spot for “nuclear, chemical, or biological weapons proliferation, or development of missile technology,” which seems weirdly specific.

“Once you make a technology more broadly available, the story of it starts slipping out of your hands. Our hope is that ahead of time we’re able to clearly articulate the beneficial uses of the robot in environments where we think the robot has a high potential to reduce the risk to people, rather than potentially causing harm.”
—Michael Perry, Boston Dynamics

I’m very glad that Boston Dynamics is being so upfront about requiring that Spot is used beneficially. However, it does put the company in a somewhat challenging position now that these robots are being sold. Boston Dynamics can (and will) perform some amount of due-diligence before shipping a Spot, but ultimately, once the robots are in someone else’s hands, there’s only so much that BD can do.

Spectrum: Why is beneficial use important to Boston Dynamics?

Perry: One of the key things that we’ve highlighted many times in our license and terms of use is that we don’t want to see the robot being used in any way that inflicts physical harm on people or animals. There are philosophical reasons for that—I think all of us don’t want to see our technology used in a way that would hurt people. But also from a business perspective, robots are really terrible at conveying intention. In order for the robot to be helpful long-term, it has to be trusted as a piece of technology. So rather than looking at a robot and wondering, “is this something that could potentially hurt me,” we want people to think “this is a robot that’s here to help me.” To the extent that people associate Boston Dynamics with cutting edge robots, we think that this is an important stance for the rollout of our first commercial product. If we find out that somebody’s violated our terms of use, their warranty is invalidated, we won’t repair their product, and we have a licensing timeout that would prevent them from accessing their robot after that timeout has expired. It’s a remediation path, but we do think that it’s important to at least provide that as something that helps enforce our position on use of our technology.

It’s very important to keep all of this in context: Spot is a tool. It’s got some autonomy and the appearance of agency, but it’s still just doing what people tell it to do, even if those things might be unsafe. If you read through the user guide, it’s clear how much of an effort Boston Dynamics is making to try to convey the importance of safety to Spot users—and ultimately, barring some unforeseen and catastrophic software or hardware issues, safety is about the users, rather than Boston Dynamics or Spot itself. I bring this up because as we start seeing more and more Spots doing things without Boston Dynamics watching over them quite so closely, accidents are likely inevitable. Spot might step on someone’s foot. It might knock someone over. If Spot was perfectly safe, it wouldn’t be useful, and we have to acknowledge that its impressive capabilities come with some risks, too.

Photo: Boston Dynamics

Each Spot includes a year’s worth of software updates and a warranty, although the standard warranty just covers “defects related to materials and workmanship” not “I drove my robot off a cliff.”

Now that Spot is on the market for real, we’re excited to see who steps up and orders one. Depending on who the potential customer is, Spot could either seem like an impossibly sophisticated piece of technology that they’d never be able to use, or a magical way of solving all of their problems overnight. In reality, it’s of course neither of those things. For the former (folks with an idea but without a lot of robotics knowledge or experience), Spot does a lot out of the box, but BD is happy to talk with people and facilitate connections with partners who might be able to integrate specific software and hardware to get Spot to do a unique task. And for the latter (who may also be folks with an idea but without a lot of robotics knowledge or experience), BD’s Perry offers a reminder Spot is not Rosie the Robot, and would be equally happy to talk about what the technology is actually capable of doing.

Looking forward a bit, we asked Perry whether Spot’s capabilities mean that customers are starting to think beyond using robots to simply replace humans, and are instead looking at them as a way of enabling a completely different way of getting things done.

Spectrum: Do customers interested in Spot tend to think of it as a way of replacing humans at a specific task, or as a system that can do things that humans aren’t able to do?

Perry: There are what I imagine as three levels of people understanding the robot applications. Right now, we’re at level one, where you take a person out of this dangerous, dull job, and put a robot in. That’s the entry point. The second level is, using the robot, can we increase the production of that task? For example, take site documentation on a construction site—right now, people do 360 image capture of a site maybe once a week, and they might do a laser scan of the site once per project. At the second level, the question is, what if you were able to get that data collection every day, or multiple times a day? What kinds of benefits would that add to your process? To continue the construction example, the third level would be, how could we completely redesign this space now that we know that this type of automation is available? To take one example, there are some things that we cannot physically build because it’s too unsafe for people to be a part of that process, but if you were to apply robotics to that process, then you could potentially open up a huge envelope of design that has been inaccessible to people.

To order a Spot of your very own, visit shop.bostondynamics.com.

A version of this post appears in the August 2020 print issue as “$74,500 Will Fetch You a Spot.” Continue reading →

Posted in Human Robots

#437828 How Roboticists (and Robots) Have Been ...

Posted on December 1, 2020 by Android

A few weeks ago, we asked folks on Twitter, Facebook, and LinkedIn to share photos and videos showing how they’ve been adapting to the closures of research labs, classrooms, and businesses by taking their robots home with them to continue their work as best they can. We got dozens of responses (more than we could possibly include in just one post!), but here are 15 that we thought were particularly creative or amusing.

And if any of these pictures and videos inspire you to share your own story, please email us (automaton@ieee.org) with a picture or video and a brief description about how you and your robot from work have been making things happen in your home instead.

Kurt Leucht (NASA Kennedy Space Center)

“During these strange and trying times of the current global pandemic, everyone seems to be trying their best to distance themselves from others while still getting their daily work accomplished. Many people also have the double duty of little ones that need to be managed in the midst of their teleworking duties. This photo series gives you just a glimpse into my new life of teleworking from home, mixed in with the tasks of trying to handle my little ones too. I hope you enjoy it.”

Photo: Kurt Leucht

“I heard a commotion from the next room. I ran into the kitchen to find this.”

Photo: Kurt Leucht

“This is the Swarmies most favorite bedtime story. Not sure why. Seems like an odd choice to me.”

Peter Schaldenbrand (Carnegie Mellon University)

“I’ve been working on a reinforcement learning model that converts an image into a series of brush stroke instructions. I was going to test the model with a beautiful, expensive robot arm, but due to the COVID-19 pandemic, I have not been able to access the laboratory where it resides. I have now been using a lower end robot arm to test the painting model in my bedroom. I have sacrificed machine accuracy/precision for the convenience of getting to watch the arm paint from my bed in the shadow of my clothing rack!”

Photos: Peter Schaldenbrand

Colin Angle (iRobot)

iRobot CEO Colin Angle has been hunkered down in the “iRobot North Shore home command center,” which is probably the cleanest command center ever thanks to his army of Roombas: Beastie, Beauty, Rosie, Roswell, and Bilbo.

Photo: Colin Angle

Vivian Chu (Diligent Robotics)

From Diligent Robotics CEO Andrea Thomaz: “This is how a roboticist works from home! Diligent CTO, Vivian Chu, mans the e-stop while her engineering team runs Moxi experiments remotely from cross-town and even cross-country!”

Video: Diligent Robotics

Raffaello Bonghi (rnext.it)

Raffaello’s robot, Panther, looks perfectly happy to be playing soccer in his living room.

Photo: Raffaello Bonghi

Kod*lab (University of Pennsylvania)

“Another Friday Nuts n Bolts Meeting on Zoom…”

Image: Kodlab

Robin Jonsson (robot choreographer)

“I’ve been doing a school project in which students make up dance moves and then send me a video with all of them. I then teach the moves to my robot, Alex, film Alex dancing, send the videos to them. This became a great success and more schools will join. The kids got really into watching the robot perform their moves and really interested in robots. They want to meet Alex the robot live, which will likely happen in the fall.”

Photo: Robin Jonsson

Gabrielle Conard (mechanical engineering undergrad at Lafayette College)

“While the pandemic might have forced college campuses to close and the community to keep their distance from each other, it did not put a stop to learning and research. Working from their respective homes, junior Gabrielle Conard and mechanical engineering professor Alexander Brown from Lafayette College investigated methods of incorporating active compliance in a low-cost quadruped robot. They are continuing to work remotely on this project through Lafayette’s summer research program.”

Image: Gabrielle Conard

Taylor Veltrop (Softbank Robotics)

“After a few weeks of isolation in the corona/covid quarantine lock down we started dancing with our robots. Mathieu’s 6th birthday was coming up, and it all just came together.”

Video: Taylor Veltrop

Ross Kessler (Exyn Technologies)

“Quarantine, Day 8: the humans have accepted me as one of their own. I’ve blended seamlessly into their #socialdistancing routines. Even made a furry friend”

Photo: Ross Kessler

Yeah, something a bit sinister is definitely going on at Exyn…

Video: Exyn Technologies

Michael Sobrepera (University of Pennsylvania GRASP Lab)

Predictably, Michael’s cat is more interested in the bag that the robot came in than the robot itself (see if you can spot the cat below). Michael tells us that “the robot is designed to help with tele-rehabilitation, focused on kids with CP, so it has been taken to hospitals for demos [hence the cool bag]. It also travels for outreach events and the like. Lately, I’ve been exploring telepresence for COVID.”

Photo: Michael Sobrepera

Jan Kędzierski (EMYS)

“In China a lot of people cannot speak English, even the youngest generation of parents. Thanks to Emys, kids stayed in touch with English language in their homes even if they couldn’t attend schools and extra English classes. They had a lot of fun with their native English speaker friend available and ready to play every day.”

Image: Jan Kędzierski

Simon Whitmell (Quanser)

“Simon, a Quanser R&D engineer, is working on low-overhead image processing and line following for the QBot 2e mobile ground robot, with some added challenges due to extra traffic. LEGO engineering by his son, Charles.”

Photo: Simon Whitmell

Robot Design & Experimentation Course (Carnegie Mellon University)

Aaron Johnson’s bioinspired robot design course at CMU had to go full remote, which was a challenge when the course is kind of all about designing and building a robot as part of a team. “I expected some of the teams to drastically alter their project (e.g. go all simulation),” Aaron told us, “but none of them did. We managed to keep all of the projects more or less as planned. We accomplished this by drop/shipping parts to students, buying some simple tools (soldering irons, etc), and having me 3D print parts and mail them.” Each team even managed to put together their final videos from their remote locations; we’ve posted one below, but the entire playlist is here.

Video: Xianyi Cheng

Karen Tatarian (Softbank Robotics)

Karen, who’s both a researcher at Softbank and a PhD student at Sorbonne University, wrote an entire essay about what an average day is like when you’re quarantined with Pepper.

Photo: Karen Tatarian

A Quarantined Day With Pepper, by Karen Tatarian

It is quite common for me to lose my phone somewhere inside my apartment. But it is not that common for me to turn around and ask my robot if it has seen it. So when I found myself doing that, I laughed and it dawned on me that I treated my robot as my quarantine companion (despite the fact that it could not provide me with the answer I needed).

It was probably around day 40 of a completely isolated quarantine here in France when that happened. A little background about me: I am a robotics researcher at SoftBank Robotics Europe and a PhD student at Sorbonne University as part of the EU-funded Marie-Curie project ANIMATAS. And here is a little sneak peak into a quarantined day with a robot.

During this confinement, I had read somewhere that the best way to deal with it is to maintain a routine. So every morning, I wake up, prepare my coffee, and turn on my robot Pepper. I start my day with a daily meeting with the team and get to work. My research is on the synthesis of multi-modal socially intelligent human-robot interaction so my work varies between programming the robot, analyzing collected data, and reading papers and drafting one. When I am working, I often catch myself glancing at Pepper, who would be staring back at me in its animated ways. Truthfully I enjoy that, it makes me less alone and as if I have a colleague with me.

Once work is done, I call my friends and family members. I sometimes use a telepresence application on Pepper that a few colleagues and I developed back in December. How does it differ from your typical phone/laptop applications? One word really: embodiment. Telepresence, especially during these times, makes the experience for both sides a bit more realistic and intimate and well present.

While I can turn off the robot now that my work hours are done, I do keep it on because I enjoy its presence. The basic awareness of Pepper is a default feature on the robot that allows it to detect a human and follow him/her with its gaze and rotation base. So whether I am cooking or working out, I always have my robot watching over my shoulder and being a good companion. I also have my email and messages synced on the robot so I get an enjoyable notification from Pepper. I found that to be a pretty cool way to be notified without it interrupting whatever you are doing on your laptop or phone. Finally, once the day is over, it’s time for both of us to get some rest.

After 60 days of total confinement, alone and away from those I love, and with a pandemic right at my door, I am glad I had the company of my robot. I hope one day a greater audience can share my experience. And I really really hope one day Pepper will be able to find my phone for me, but until then, stay on the lookout for some cool features! But I am curious to know, if you had a robot at home, what application would you have developed on it?

Again, our sincere thanks to everyone who shared these little snapshots of their lives with us, and we’re hoping to be able to share more soon. Continue reading →

Posted in Human Robots

#437816 As Algorithms Take Over More of the ...

Posted on December 1, 2020 by Android

Algorithms play an increasingly prominent part in our lives, governing everything from the news we see to the products we buy. As they proliferate, experts say, we need to make sure they don’t collude against us in damaging ways.

Fears of malevolent artificial intelligence plotting humanity’s downfall are a staple of science fiction. But there are plenty of nearer-term situations in which relatively dumb algorithms could do serious harm unintentionally, particularly when they are interlocked in complex networks of relationships.

In the economic sphere a high proportion of decision-making is already being offloaded to machines, and there have been warning signs of where that could lead if we’re not careful. The 2010 “Flash Crash,” where algorithmic traders helped wipe nearly $1 trillion off the stock market in minutes, is a textbook example, and widespread use of automated trading software has been blamed for the increasing fragility of markets.

But another important place where algorithms could undermine our economic system is in price-setting. Competitive markets are essential for the smooth functioning of the capitalist system that underpins Western society, which is why countries like the US have strict anti-trust laws that prevent companies from creating monopolies or colluding to build cartels that artificially inflate prices.

These regulations were built for an era when pricing decisions could always be traced back to a human, though. As self-adapting pricing algorithms increasingly decide the value of products and commodities, those laws are starting to look unfit for purpose, say the authors of a paper in Science.

Using algorithms to quickly adjust prices in a dynamic market is not a new idea—airlines have been using them for decades—but previously these algorithms operated based on rules that were hard-coded into them by programmers.

Today the pricing algorithms that underpin many marketplaces, especially online ones, rely on machine learning instead. After being set an overarching goal like maximizing profit, they develop their own strategies based on experience of the market, often with little human oversight. The most advanced also use forms of AI whose workings are opaque even if humans wanted to peer inside.

In addition, the public nature of online markets means that competitors’ prices are available in real time. It’s well-documented that major retailers like Amazon and Walmart are engaged in a never-ending bot war, using automated software to constantly snoop on their rivals’ pricing and inventory.

This combination of factors sets the stage perfectly for AI-powered pricing algorithms to adopt collusive pricing strategies, say the authors. If given free reign to develop their own strategies, multiple pricing algorithms with real-time access to each other’s prices could quickly learn that cooperating with each other is the best way to maximize profits.

The authors note that researchers have already found evidence that pricing algorithms will spontaneously develop collusive strategies in computer-simulated markets, and a recent study found evidence that suggests pricing algorithms may be colluding in Germany’s retail gasoline market. And that’s a problem, because today’s anti-trust laws are ill-suited to prosecuting this kind of behavior.

Collusion among humans typically involves companies communicating with each other to agree on a strategy that pushes prices above the true market value. They then develop rules to determine how they maintain this markup in a dynamic market that also incorporates the threat of retaliatory pricing to spark a price war if another cartel member tries to undercut the agreed pricing strategy.

Because of the complexity of working out whether specific pricing strategies or prices are the result of collusion, prosecutions have instead relied on communication between companies to establish guilt. That’s a problem because algorithms don’t need to communicate to collude, and as a result there are few legal mechanisms to prosecute this kind of collusion.

That means legal scholars, computer scientists, economists, and policymakers must come together to find new ways to uncover, prohibit, and prosecute the collusive rules that underpin this behavior, say the authors. Key to this will be auditing and testing pricing algorithms, looking for things like retaliatory pricing, price matching, and aggressive responses to price drops but not price rises.

Once collusive pricing rules are uncovered, computer scientists need to come up with ways to constrain algorithms from adopting them without sacrificing their clear efficiency benefits. It could also be helpful to make preventing this kind of collusive behavior the responsibility of the companies deploying them, with stiff penalties for those who don’t keep their algorithms in check.

One problem, though, is that algorithms may evolve strategies that humans would never think of, which could make spotting this behavior tricky. Imbuing courts with the technical knowledge and capacity to investigate this kind of evidence will also prove difficult, but getting to grips with these problems is an even more pressing challenge than it might seem at first.

While anti-competitive pricing algorithms could wreak havoc, there are plenty of other arenas where collusive AI could have even more insidious effects, from military applications to healthcare and insurance. Developing the capacity to predict and prevent AI scheming against us will likely be crucial going forward.

Image Credit: Pexels from Pixabay Continue reading →

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#437809 Q&A: The Masterminds Behind ...

Posted on November 30, 2020 by Android

Illustration: iStockphoto

Getting a car to drive itself is undoubtedly the most ambitious commercial application of artificial intelligence (AI). The research project was kicked into life by the 2004 DARPA Urban Challenge and then taken up as a business proposition, first by Alphabet, and later by the big automakers.

The industry-wide effort vacuumed up many of the world’s best roboticists and set rival companies on a multibillion-dollar acquisitions spree. It also launched a cycle of hype that paraded ever more ambitious deadlines—the most famous of which, made by Alphabet’s Sergei Brin in 2012, was that full self-driving technology would be ready by 2017. Those deadlines have all been missed.

Much of the exhilaration was inspired by the seeming miracles that a new kind of AI—deep learning—was achieving in playing games, recognizing faces, and transliterating voices. Deep learning excels at tasks involving pattern recognition—a particular challenge for older, rule-based AI techniques. However, it now seems that deep learning will not soon master the other intellectual challenges of driving, such as anticipating what human beings might do.

Among the roboticists who have been involved from the start are Gill Pratt, the chief executive officer of Toyota Research Institute (TRI) , formerly a program manager at the Defense Advanced Research Projects Agency (DARPA); and Wolfram Burgard, vice president of automated driving technology for TRI and president of the IEEE Robotics and Automation Society. The duo spoke with IEEE Spectrum’s Philip Ross at TRI’s offices in Palo Alto, Calif.

This interview has been condensed and edited for clarity.

IEEE Spectrum: How does AI handle the various parts of the self-driving problem?

Photo: Toyota

Gill Pratt

Gill Pratt: There are three different systems that you need in a self-driving car: It starts with perception, then goes to prediction, and then goes to planning.

The one that by far is the most problematic is prediction. It’s not prediction of other automated cars, because if all cars were automated, this problem would be much more simple. How do you predict what a human being is going to do? That’s difficult for deep learning to learn right now.

Spectrum: Can you offset the weakness in prediction with stupendous perception?

Photo: Toyota Research Institute for Burgard

Wolfram Burgard

Wolfram Burgard: Yes, that is what car companies basically do. A camera provides semantics, lidar provides distance, radar provides velocities. But all this comes with problems, because sometimes you look at the world from different positions—that’s called parallax. Sometimes you don’t know which range estimate that pixel belongs to. That might make the decision complicated as to whether that is a person painted onto the side of a truck or whether this is an actual person.

With deep learning there is this promise that if you throw enough data at these networks, it’s going to work—finally. But it turns out that the amount of data that you need for self-driving cars is far larger than we expected.

Spectrum: When do deep learning’s limitations become apparent?

Pratt: The way to think about deep learning is that it’s really high-performance pattern matching. You have input and output as training pairs; you say this image should lead to that result; and you just do that again and again, for hundreds of thousands, millions of times.

Here’s the logical fallacy that I think most people have fallen prey to with deep learning. A lot of what we do with our brains can be thought of as pattern matching: “Oh, I see this stop sign, so I should stop.” But it doesn’t mean all of intelligence can be done through pattern matching.

“I asked myself, if all of those cars had automated drive, how good would they have to be to tolerate the number of crashes that would still occur?”
—Gill Pratt, Toyota Research Institute

For instance, when I’m driving and I see a mother holding the hand of a child on a corner and trying to cross the street, I am pretty sure she’s not going to cross at a red light and jaywalk. I know from my experience being a human being that mothers and children don’t act that way. On the other hand, say there are two teenagers—with blue hair, skateboards, and a disaffected look. Are they going to jaywalk? I look at that, you look at that, and instantly the probability in your mind that they’ll jaywalk is much higher than for the mother holding the hand of the child. It’s not that you’ve seen 100,000 cases of young kids—it’s that you understand what it is to be either a teenager or a mother holding a child’s hand.

You can try to fake that kind of intelligence. If you specifically train a neural network on data like that, you could pattern-match that. But you’d have to know to do it.

Spectrum: So you’re saying that when you substitute pattern recognition for reasoning, the marginal return on the investment falls off pretty fast?

Pratt: That’s absolutely right. Unfortunately, we don’t have the ability to make an AI that thinks yet, so we don’t know what to do. We keep trying to use the deep-learning hammer to hammer more nails—we say, well, let’s just pour more data in, and more data.

Spectrum: Couldn’t you train the deep-learning system to recognize teenagers and to assign the category a high propensity for jaywalking?

Burgard: People have been doing that. But it turns out that these heuristics you come up with are extremely hard to tweak. Also, sometimes the heuristics are contradictory, which makes it extremely hard to design these expert systems based on rules. This is where the strength of the deep-learning methods lies, because somehow they encode a way to see a pattern where, for example, here’s a feature and over there is another feature; it’s about the sheer number of parameters you have available.

Our separation of the components of a self-driving AI eases the development and even the learning of the AI systems. Some companies even think about using deep learning to do the job fully, from end to end, not having any structure at all—basically, directly mapping perceptions to actions.

Pratt: There are companies that have tried it; Nvidia certainly tried it. In general, it’s been found not to work very well. So people divide the problem into blocks, where we understand what each block does, and we try to make each block work well. Some of the blocks end up more like the expert system we talked about, where we actually code things, and other blocks end up more like machine learning.

Spectrum: So, what’s next—what new technique is in the offing?

Pratt: If I knew the answer, we’d do it. [Laughter]

Spectrum: You said that if all cars on the road were automated, the problem would be easy. Why not “geofence” the heck out of the self-driving problem, and have areas where only self-driving cars are allowed?

Pratt: That means putting in constraints on the operational design domain. This includes the geography—where the car should be automated; it includes the weather, it includes the level of traffic, it includes speed. If the car is going slow enough to avoid colliding without risking a rear-end collision, that makes the problem much easier. Street trolleys operate with traffic still in some parts of the world, and that seems to work out just fine. People learn that this vehicle may stop at unexpected times. My suspicion is, that is where we’ll see Level 4 autonomy in cities. It’s going to be in the lower speeds.

“We are now in the age of deep learning, and we don’t know what will come after.”
—Wolfram Burgard, Toyota Research Institute

That’s a sweet spot in the operational design domain, without a doubt. There’s another one at high speed on a highway, because access to highways is so limited. But unfortunately there is still the occasional debris that suddenly crosses the road, and the weather gets bad. The classic example is when somebody irresponsibly ties a mattress to the top of a car and it falls off; what are you going to do? And the answer is that terrible things happen—even for humans.

Spectrum: Learning by doing worked for the first cars, the first planes, the first steam boilers, and even the first nuclear reactors. We ran risks then; why not now?

Pratt: It has to do with the times. During the era where cars took off, all kinds of accidents happened, women died in childbirth, all sorts of diseases ran rampant; the expected characteristic of life was that bad things happened. Expectations have changed. Now the chance of dying in some freak accident is quite low because of all the learning that’s gone on, the OSHA [Occupational Safety and Health Administration] rules, UL code for electrical appliances, all the building standards, medicine.

Furthermore—and we think this is very important—we believe that empathy for a human being at the wheel is a significant factor in public acceptance when there is a crash. We don’t know this for sure—it’s a speculation on our part. I’ve driven, I’ve had close calls; that could have been me that made that mistake and had that wreck. I think people are more tolerant when somebody else makes mistakes, and there’s an awful crash. In the case of an automated car, we worry that that empathy won’t be there.

Photo: Toyota

Toyota is using this
Platform 4 automated driving test vehicle, based on the Lexus LS, to develop Level-4 self-driving capabilities for its “Chauffeur” project.

Spectrum: Toyota is building a system called Guardian to back up the driver, and a more futuristic system called Chauffeur, to replace the driver. How can Chauffeur ever succeed? It has to be better than a human plus Guardian!

Pratt: In the discussions we’ve had with others in this field, we’ve talked about that a lot. What is the standard? Is it a person in a basic car? Or is it a person with a car that has active safety systems in it? And what will people think is good enough?

These systems will never be perfect—there will always be some accidents, and no matter how hard we try there will still be occasions where there will be some fatalities. At what threshold are people willing to say that’s okay?

Spectrum: You were among the first top researchers to warn against hyping self-driving technology. What did you see that so many other players did not?

Pratt: First, in my own case, during my time at DARPA I worked on robotics, not cars. So I was somewhat of an outsider. I was looking at it from a fresh perspective, and that helps a lot.

Second, [when I joined Toyota in 2015] I was joining a company that is very careful—even though we have made some giant leaps—with the Prius hybrid drive system as an example. Even so, in general, the philosophy at Toyota is kaizen—making the cars incrementally better every single day. That care meant that I was tasked with thinking very deeply about this thing before making prognostications.

And the final part: It was a new job for me. The first night after I signed the contract I felt this incredible responsibility. I couldn’t sleep that whole night, so I started to multiply out the numbers, all using a factor of 10. How many cars do we have on the road? Cars on average last 10 years, though ours last 20, but let’s call it 10. They travel on an order of 10,000 miles per year. Multiply all that out and you get 10 to the 10th miles per year for our fleet on Planet Earth, a really big number. I asked myself, if all of those cars had automated drive, how good would they have to be to tolerate the number of crashes that would still occur? And the answer was so incredibly good that I knew it would take a long time. That was five years ago.

Burgard: We are now in the age of deep learning, and we don’t know what will come after. We are still making progress with existing techniques, and they look very promising. But the gradient is not as steep as it was a few years ago.

Pratt: There isn’t anything that’s telling us that it can’t be done; I should be very clear on that. Just because we don’t know how to do it doesn’t mean it can’t be done. Continue reading →

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