Tag Archives: philosophy

#438886 This Week’s Awesome Tech Stories From ...

Posted on March 14, 2021 by Android

ARTIFICIAL INTELLIGENCE
This Chip for AI Works Using Light, Not Electrons
Will Knight | Wired
“As demand for artificial intelligence grows, so does hunger for the computer power needed to keep AI running. Lightmatter, a startup born at MIT, is betting that AI’s voracious hunger will spawn demand for a fundamentally different kind of computer chip—one that uses light to perform key calculations. ‘Either we invent new kinds of computers to continue,’ says Lightmatter CEO Nick Harris, ‘or AI slows down.’i”

BIOTECH
With This CAD for Genomes, You Can Design New Organisms
Eliza Strickland | IEEE Spectrum
“Imagine being able to design a new organism as easily as you can design a new integrated circuit. That’s the ultimate vision behind the computer-aided design (CAD) program being developed by the GP-write consortium. ‘We’re taking the same things we’d do for design automation in electronics, and applying them to biology,’ says Doug Densmore, an associate professor of electrical and computer engineering at Boston University.”

BIOLOGY
Hey, So These Sea Slugs Decapitate Themselves and Grow New Bodies
Matt Simon | Wired
“That’s right: It pulled a Deadpool. Just a few hours after its self-decapitation, the head began dragging itself around to feed. After a day, the neck wound had closed. After a week, it started to regenerate a heart. In less than a month, the whole body had grown back, and the disembodied slug was embodied once more.”

INTERNET
Move Over, Deep Nostalgia, This AI App Can Make Kim Jong-un Sing ‘I Will Survive’
Helen Sullivan | The Guardian
“If you’ve ever wanted to know what it might be like to see Kim Jong-un let loose at karaoke, your wish has been granted, thanks to an app that lets users turn photographs of anyone—or anything remotely resembling a face—into uncanny AI-powered videos of them lip syncing famous songs.”

ENERGY
GM Unveils Plans for Lithium-Metal Batteries That Could Boost EV Range
Steve Dent | Engadget
“GM has released more details about its next-generation Ultium batteries, including plans for lithium-metal (Li-metal) technology to boost performance and energy density. The automaker announced that it has signed an agreement to work with SolidEnergy Systems (SES), an MIT spinoff developing prototype Li-metal batteries with nearly double the capacity of current lithium-ion cells.”

TECHNOLOGY
Xi’s Gambit: China Plans for a World Without American Technology
Paul Mozur and Steven Lee Myers | The New York Times
“China is freeing up tens of billions of dollars for its tech industry to borrow. It is cataloging the sectors where the United States or others could cut off access to crucial technologies. And when its leaders released their most important economic plans last week, they laid out their ambitions to become an innovation superpower beholden to none.”

SCIENCE
Imaginary Numbers May Be Essential for Describing Reality
Charlie Wood | Wired
“…physicists may have just shown for the first time that imaginary numbers are, in a sense, real. A group of quantum theorists designed an experiment whose outcome depends on whether nature has an imaginary side. Provided that quantum mechanics is correct—an assumption few would quibble with—the team’s argument essentially guarantees that complex numbers are an unavoidable part of our description of the physical universe.”

PHILOSOPHY
What Is Life? Its Vast Diversity Defies Easy Definition
Carl Zimmer | Quanta
“i‘It is commonly said,’ the scientists Frances Westall and André Brack wrote in 2018, ‘that there are as many definitions of life as there are people trying to define it.’ …As an observer of science and of scientists, I find this behavior strange. It is as if astronomers kept coming up with new ways to define stars. …With scientists adrift in an ocean of definitions, philosophers rowed out to offer lifelines.”

Image Credit: Kir Simakov / Unsplash Continue reading →

Posted in Human Robots

#438769 Will Robots Make Good Friends? ...

Posted on February 26, 2021 by Android

In the 2012 film Robot and Frank, the protagonist, a retired cat burglar named Frank, is suffering the early symptoms of dementia. Concerned and guilty, his son buys him a “home robot” that can talk, do household chores like cooking and cleaning, and remind Frank to take his medicine. It’s a robot the likes of which we’re getting closer to building in the real world.

The film follows Frank, who is initially appalled by the idea of living with a robot, as he gradually begins to see the robot as both functionally useful and socially companionable. The film ends with a clear bond between man and machine, such that Frank is protective of the robot when the pair of them run into trouble.

This is, of course, a fictional story, but it challenges us to explore different kinds of human-to-robot bonds. My recent research on human-robot relationships examines this topic in detail, looking beyond sex robots and robot love affairs to examine that most profound and meaningful of relationships: friendship.

My colleague and I identified some potential risks, like the abandonment of human friends for robotic ones, but we also found several scenarios where robotic companionship can constructively augment people’s lives, leading to friendships that are directly comparable to human-to-human relationships.

Philosophy of Friendship
The robotics philosopher John Danaher sets a very high bar for what friendship means. His starting point is the “true” friendship first described by the Greek philosopher Aristotle, which saw an ideal friendship as premised on mutual good will, admiration, and shared values. In these terms, friendship is about a partnership of equals.

Building a robot that can satisfy Aristotle’s criteria is a substantial technical challenge and is some considerable way off, as Danaher himself admits. Robots that may seem to be getting close, such as Hanson Robotics’ Sophia, base their behavior on a library of pre-prepared responses: a humanoid chatbot, rather than a conversational equal. Anyone who’s had a testing back-and-forth with Alexa or Siri will know AI still has some way to go in this regard.

Aristotle also talked about other forms of “imperfect” friendship, such as “utilitarian” and “pleasure” friendships, which are considered inferior to true friendship because they don’t require symmetrical bonding and are often to one party’s unequal benefit. This form of friendship sets a relatively very low bar which some robots, like “sexbots” and robotic pets, clearly already meet.

Artificial Amigos
For some, relating to robots is just a natural extension of relating to other things in our world, like people, pets, and possessions. Psychologists have even observed how people respond naturally and socially towards media artefacts like computers and televisions. Humanoid robots, you’d have thought, are more personable than your home PC.

However, the field of “robot ethics” is far from unanimous on whether we can—or should— develop any form of friendship with robots. For an influential group of UK researchers who charted a set of “ethical principles of robotics,” human-robot “companionship” is an oxymoron, and to market robots as having social capabilities is dishonest and should be treated with caution, if not alarm. For these researchers, wasting emotional energy on entities that can only simulate emotions will always be less rewarding than forming human-to-human bonds.

But people are already developing bonds with basic robots, like vacuum-cleaning and lawn-trimming machines that can be bought for less than the price of a dishwasher. A surprisingly large number of people give these robots pet names—something they don’t do with their dishwashers. Some even take their cleaning robots on holiday.

Other evidence of emotional bonds with robots include the Shinto blessing ceremony for Sony Aibo robot dogs that were dismantled for spare parts, and the squad of US troops who fired a 21-gun salute, and awarded medals, to a bomb-disposal robot named “Boomer” after it was destroyed in action.

These stories, and the psychological evidence we have so far, make clear that we can extend emotional connections to things that are very different to us, even when we know they are manufactured and pre-programmed. But do those connections constitute a friendship comparable to that shared between humans?

True Friendship?
A colleague and I recently reviewed the extensive literature on human-to-human relationships to try to understand how, and if, the concepts we found could apply to bonds we might form with robots. We found evidence that many coveted human-to-human friendships do not in fact live up to Aristotle’s ideal.

We noted a wide range of human-to-human relationships, from relatives and lovers to parents, carers, service providers, and the intense (but unfortunately one-way) relationships we maintain with our celebrity heroes. Few of these relationships could be described as completely equal and, crucially, they are all destined to evolve over time.

All this means that expecting robots to form Aristotelian bonds with us is to set a standard even human relationships fail to live up to. We also observed forms of social connectedness that are rewarding and satisfying and yet are far from the ideal friendship outlined by the Greek philosopher.

We know that social interaction is rewarding in its own right, and something that, as social mammals, humans have a strong need for. It seems probable that relationships with robots could help to address the deep-seated urge we all feel for social connection—like providing physical comfort, emotional support, and enjoyable social exchanges—currently provided by other humans.

Our paper also discussed some potential risks. These arise particularly in settings where interaction with a robot could come to replace interaction with people, or where people are denied a choice as to whether they interact with a person or a robot—in a care setting, for instance.

These are important concerns, but they’re possibilities and not inevitabilities. In the literature we reviewed we actually found evidence of the opposite effect: robots acting to scaffold social interactions with others, acting as ice-breakers in groups, and helping people to improve their social skills or to boost their self-esteem.

It appears likely that, as time progresses, many of us will simply follow Frank’s path towards acceptance: scoffing at first, before settling into the idea that robots can make surprisingly good companions. Our research suggests that’s already happening—though perhaps not in a way of which Aristotle would have approved.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Andy Kelly on Unsplash Continue reading →

Posted in Human Robots

#438080 Boston Dynamics’ Spot Robot Is Now ...

Posted on February 3, 2021 by Android

Boston Dynamics has been working on an arm for its Spot quadruped for at least five years now. There have been plenty of teasers along the way, including this 45-second clip from early 2018 of Spot using its arm to open a door, which at 85 million views seems to be Boston Dynamics’ most popular video ever by a huge margin. Obviously, there’s a substantial amount of interest in turning Spot from a highly dynamic but mostly passive sensor platform into a mobile manipulator that can interact with its environment.

As anyone who’s done mobile manipulation will tell you, actually building an arm is just the first step—the really tricky part is getting that arm to do exactly what you want it to do. In particular, Spot’s arm needs to be able to interact with the world with some amount of autonomy in order to be commercially useful, because you can’t expect a human (remote or otherwise) to spend all their time positioning individual joints or whatever to pick something up. So the real question about this arm is whether Boston Dynamics has managed to get it to a point where it’s autonomous enough that users with relatively little robotics experience will be able to get it to do useful tasks without driving themselves nuts.

Today, Boston Dynamics is announcing commercial availability of the Spot arm, along with some improved software called Scout plus a self-charging dock that’ll give the robot even more independence. And to figure out exactly what Spot’s new arm can do, we spoke with Zachary Jackowski, Spot Chief Engineer at Boston Dynamics.

Although Boston Dynamics’ focus has been on dynamic mobility and legged robots, the company has been working on manipulation for a very long time. We first saw an arm prototype on an early iteration of Spot in 2016, where it demonstrated some impressive functionality, including loading a dishwasher and fetching a beer in a way that only resulted in a minor catastrophe. But we’re guessing that Spot’s arm can trace its history back to BigDog’s crazy powerful hydraulic face-arm, which was causing mayhem with cinder blocks back in 2013:

Spot’s arm is not quite that powerful (it has to drag cinder blocks along the ground rather than fling them into space), but you can certainly see the resemblance. Here’s the video that Boston Dynamics posted yesterday to introduce Spot’s new arm:

A couple of things jumped out from this video right away. First, Spot is doing whole body manipulation with its arm, as opposed to just acting as a four-legged base that brings the arm where it needs to go. Planning looks to be very tightly integrated, such that if you ask the robot to manipulate an object, its arm, legs, and torso all work together to optimize that manipulation. Also, when Spot flips that electrical switch, you see the robot successfully grasp the switch, and then reposition its body in a way that looks like it provides better leverage for the flip, which is a neat trick. It looks like it may be able to use the strength of its legs to augment the strength of its arm, as when it’s dragging the cinder block around, which is surely an homage to BigDog. The digging of a hole is particularly impressive. But again, the real question is how much of this is autonomous or semi-autonomous in a way that will be commercially useful?

Before we get to our interview with Spot Chief Engineer Zack Jackowski, it’s worth watching one more video that Boston Dynamics shared with us:

This is notable because Spot is opening a door that’s not ADA compliant, and the robot is doing it with a simple two-finger gripper. Most robots you see interacting with doors rely on ADA compliant hardware, meaning (among other things) a handle that can be pushed rather than a knob that has to be twisted, because it’s much more challenging for a robot to grasp and twist a smooth round door knob than it is to just kinda bash down on a handle. That capability, combined with Spot being able to pass through a spring-loaded door, potentially opens up a much wider array of human environments to the robot, and that’s where we started our conversation with Jackowski.

IEEE Spectrum: At what point did you decide that for Spot’s arm to be useful, it had to be able to handle round door knobs?

Zachary Jackowski: We're like a lot of roboticists, where someone in a meeting about manipulation would say “it's time for the round doorknob” and people would start groaning a little bit. But the reality is that, in order to make a robot useful, you have to engage with the environments that users have. Spot’s arm uses a very simple gripper—it’s a one degree of freedom gripper, but a ton of thought has gone into all of the fine geometric contours of it such that it can grab that ADA compliant lever handle, and it’ll also do an enclosing grasp around a round door knob. The major point of a robot like Spot is to engage with the environment you have, and so you can’t cut out stuff like round door knobs.

We're thrilled to be launching the arm and getting it out with users and to have them start telling us what doors it works really well on, and what they're having trouble with. And we're going to be working on rapidly improving all this stuff. We went through a few campaigns of like, “this isn’t ready until we can open every single door at Boston Dynamics!” But every single door at Boston Dynamics and at our test lab is a small fraction of all the doors in the world. So we're prepared to learn a lot this year.

When we see Spot open a door, or when it does those other manipulation behaviors in the launch video, how much of that is autonomous, how much is scripted, and to what extent is there a human in the loop?

All of the scenes where the robot does a pick, like the snow scene or the laundry scene, that is actually an almost fully integrated autonomous behavior that has a bit of a script wrapped around it. We trained a detector for an object, and the robot is identifying that object in the environment, picking it, and putting it in the bin all autonomously. The scripted part of that is telling the robot to perform a series of picks.

One of the things that we’re excited about, and that roboticists have been excited about going back probably all the way to the DRC, is semi-autonomous manipulation. And so we have modes built into the interface where if you see an object that you want the robot to grab, all you have to do is tap that object on the screen, and the robot will walk up to it, use the depth camera in its gripper to capture a depth map, and plan a grasp on its own in real time. That’s all built-in, too.

The jump rope—robots don’t just go and jump rope on their own. We scripted an arm motion to move the rope, and wrote a script using our API to coordinate all three robots. Drawing “Boston Dynamics” in chalk in our parking lot was scripted also. One of our engineers wrote a really cool G-code interpreter that vectorizes graphics so that Spot can draw them.

So for an end user, if you wanted Spot to autonomously flip some switches for you, you’d just have to train Spot on your switches, and then Spot could autonomously perform the task?

There are a couple of ways that task could break down depending on how you’re interfacing with the robot. If you’re a tablet user, you’d probably just identify the switch yourself on the tablet’s screen, and the robot will figure out the grasp, and grasp it. Then you’ll enter a constrained manipulation mode on the tablet, and the robot will be able to actuate the switch. But the robot will take care of the complicated controls aspects, like figuring out how hard it has to pull, the center of rotation of the switch, and so on.

The video of Spot digging was pretty cool—how did that work?

That’s mostly a scripted behavior. There are some really interesting control systems topics in there, like how you’d actually do the right kinds of force control while you insert the trowel into the dirt, and how to maintain robot stability while you do it. The higher level task of how to make a good hole in the dirt—that’s scripted. But the part of the problem that’s actually digging, you need the right control system to actually do that, or you’ll dig your trowel into the ground and flip your robot over.

The last time we saw Boston Dynamics robots flipping switches and turning valves I think might have been during the DRC in 2015, when they had expert robot operators with control over every degree of freedom. How are things different now with Spot, and will non-experts in the commercial space really be able to get the robot to do useful tasks?

A lot of the things, like “pick the stuff up in the room,” or ‘turn that switch,” can all be done by a lightly trained operator using just the tablet interface. If you want to actually command all of Spot’s arm degrees of freedom, you can do that— not through the tablet, but the API does expose all of it. That’s actually a notable difference from the base robot; we’ve never opened up the part of the API that lets you command individual leg degrees of freedom, because we don’t think it’s productive for someone to do that. The arm is a little bit different. There are a lot of smart people working on arm motion planning algorithms, and maybe you want to plan your arm trajectory in a super precise way and then do a DRC-style interface where you click to approve it. You can do all that through the API if you want, but fundamentally, it’s also user friendly. It follows our general API design philosophy of giving you the highest level pieces of the toolbox that will enable you to solve a complex problem that we haven't thought of.

Looking back on it now, it’s really cool to see, after so many years, robots do the stuff that Gill Pratt was excited about kicking off with the DRC. And now it’s just a thing you can buy.

Is Spot’s arm safe?

You should follow the same safety rules that you’d follow when working with Spot normally, and that’s that you shouldn’t get within two meters of the robot when it’s powered on. Spot is not a cobot. You shouldn’t hug it. Fundamentally, the places where the robot is the most valuable are places where people don’t want to be, or shouldn’t be.

We’ve seen how people reacted to earlier videos of Spot using its arm—can you help us set some reasonable expectations for what this means for Spot?

You know, it gets right back to the normal assumptions about our robots that people make that aren’t quite reality. All of this manipulation work we’re doing— the robot’s really acting as a tool. Even if it’s an autonomous behavior, it’s a tool. The robot is digging a hole because it’s got a set of instructions that say “apply this much force over this much distance here, here, and here.”

It’s not digging a hole and planting a tree because it loves trees, as much as I’d love to build a robot that works like that.

Photo: Boston Dynamics

There isn’t too much to say about the dock, except that it’s a requirement for making Spot long-term autonomous. The uncomfortable looking charging contacts that Spot impales itself on also include hardwired network connectivity, which is important because Spot often comes back home with a huge amount of data that all needs to be offloaded and processed. Docking and undocking are autonomous— as soon as the robot sees the fiducial markers on the dock, auto docking is enabled and it takes one click to settle the robot down.

During a brief remote demo, we also learned some other interesting things about Spot’s updated remote interface. It’s very latency tolerant, since you don’t have to drive the robot directly (although you can if you want to). Click a point on the camera view and Spot will move there autonomously while avoiding obstacles, meaning that even if you’re dealing with seconds of lag, the robot will continue making safe progress. This will be especially important if (when?) Spot starts exploring the Moon.

The remote interface also has an option to adjust how close Spot can get to obstacles, or to turn the obstacle avoidance off altogether. The latter functionality is useful if Spot sees something as an obstacle that really isn’t, like a curtain, while the former is useful if the robot is operating in an environment where it needs to give an especially wide berth to objects that could be dangerous to run into. “The robot’s not perfect—robots will never be perfect,” Jackowski reminds us, which is something we really (seriously) appreciate hearing from folks working on powerful, dynamic robots. “No matter how good the robot is, you should always de-risk as much as possible.”

Another part of that de-risking is having the user let Spot know when it’s about to go up or down some stairs by putting into “Stair Mode” with a toggle switch in the remote interface. Stairs are still a challenge for Spot, and Stair Mode slows the robot down and encourages it to pitch its body more aggressively to get a better view of the stairs. You’re encouraged to use stair mode, and also encouraged to send Spot up and down stairs with its “head” pointing up the stairs both ways, but these are not requirements for stair navigation— if you want to, you can send Spot down stairs head first without putting it in stair mode. Jackowski says that eventually, Spot will detect stairways by itself even when not in stair mode and adjust itself accordingly, but for now, that de-risking is solidly in the hands of the user.

Spot’s sensor payload, which is what we were trying out for the demo, provided a great opportunity for us to hear Spot STOMP STOMP STOMPING all over the place, which was also an opportunity for us to ask Jackowski why they can’t make Spot a little quieter. “It’s advantageous for Spot to step a little bit hard for the same reason it’s advantageous for you to step a little bit hard if you’re walking around blindfolded—that reason is that it really lets you know where the ground is, particularly when you’re not sure what to expect.” He adds, “It’s all in the name of robustness— the robot might be a little louder, but it’s a little more sure of its footing.”

Boston Dynamics isn’t yet ready to disclose the price of an arm-equipped Spot, but if you’re a potential customer, now is the time to contact the Boston Dynamics sales team to ask them about it. As a reminder, the base model of Spot costs US $74,500, with extra sensing or compute adding a substantial premium on top of that.

There will be a livestream launch event taking place at 11am ET today, during which Boston Dynamics’ CEO Robert Playter, VP of Marketing Michael Perry, and other folks from Boston Dynamics will make presentations on this new stuff. It’ll be live at this link, or you can watch it below. Continue reading →

Posted in Human Robots

#438076 Boston Dynamics’ Spot Robot Is Now ...

Posted on February 2, 2021 by Android

Boston Dynamics has been working on an arm for its Spot quadruped for at least five years now. There have been plenty of teasers along the way, including this 45-second clip from early 2018 of Spot using its arm to open a door, which at 85 million views seems to be Boston Dynamics’ most popular video ever by a huge margin. Obviously, there’s a substantial amount of interest in turning Spot from a highly dynamic but mostly passive sensor platform into a mobile manipulator that can interact with its environment.

As anyone who’s done mobile manipulation will tell you, actually building an arm is just the first step—the really tricky part is getting that arm to do exactly what you want it to do. In particular, Spot’s arm needs to be able to interact with the world with some amount of autonomy in order to be commercially useful, because you can’t expect a human (remote or otherwise) to spend all their time positioning individual joints or whatever to pick something up. So the real question about this arm is whether Boston Dynamics has managed to get it to a point where it’s autonomous enough that users with relatively little robotics experience will be able to get it to do useful tasks without driving themselves nuts.

Today, Boston Dynamics is announcing commercial availability of the Spot arm, along with some improved software called Scout plus a self-charging dock that’ll give the robot even more independence. And to figure out exactly what Spot’s new arm can do, we spoke with Zachary Jackowski, Spot Chief Engineer at Boston Dynamics.

Although Boston Dynamics’ focus has been on dynamic mobility and legged robots, the company has been working on manipulation for a very long time. We first saw an arm prototype on an early iteration of Spot in 2016, where it demonstrated some impressive functionality, including loading a dishwasher and fetching a beer in a way that only resulted in a minor catastrophe. But we’re guessing that Spot’s arm can trace its history back to BigDog’s crazy powerful hydraulic face-arm, which was causing mayhem with cinder blocks back in 2013:

Spot’s arm is not quite that powerful (it has to drag cinder blocks along the ground rather than fling them into space), but you can certainly see the resemblance. Here’s the video that Boston Dynamics posted yesterday to introduce Spot’s new arm:

A couple of things jumped out from this video right away. First, Spot is doing whole body manipulation with its arm, as opposed to just acting as a four-legged base that brings the arm where it needs to go. Planning looks to be very tightly integrated, such that if you ask the robot to manipulate an object, its arm, legs, and torso all work together to optimize that manipulation. Also, when Spot flips that electrical switch, you see the robot successfully grasp the switch, and then reposition its body in a way that looks like it provides better leverage for the flip, which is a neat trick. It looks like it may be able to use the strength of its legs to augment the strength of its arm, as when it’s dragging the cinder block around, which is surely an homage to BigDog. The digging of a hole is particularly impressive. But again, the real question is how much of this is autonomous or semi-autonomous in a way that will be commercially useful?

Before we get to our interview with Spot Chief Engineer Zack Jackowski, it’s worth watching one more video that Boston Dynamics shared with us:

This is notable because Spot is opening a door that’s not ADA compliant, and the robot is doing it with a simple two-finger gripper. Most robots you see interacting with doors rely on ADA compliant hardware, meaning (among other things) a handle that can be pushed rather than a knob that has to be twisted, because it’s much more challenging for a robot to grasp and twist a smooth round door knob than it is to just kinda bash down on a handle. That capability, combined with Spot being able to pass through a spring-loaded door, potentially opens up a much wider array of human environments to the robot, and that’s where we started our conversation with Jackowski.

IEEE Spectrum: At what point did you decide that for Spot’s arm to be useful, it had to be able to handle round door knobs?

Zachary Jackowski: We're like a lot of roboticists, where someone in a meeting about manipulation would say “it's time for the round doorknob” and people would start groaning a little bit. But the reality is that, in order to make a robot useful, you have to engage with the environments that users have. Spot’s arm uses a very simple gripper—it’s a one degree of freedom gripper, but a ton of thought has gone into all of the fine geometric contours of it such that it can grab that ADA compliant lever handle, and it’ll also do an enclosing grasp around a round door knob. The major point of a robot like Spot is to engage with the environment you have, and so you can’t cut out stuff like round door knobs.

We're thrilled to be launching the arm and getting it out with users and to have them start telling us what doors it works really well on, and what they're having trouble with. And we're going to be working on rapidly improving all this stuff. We went through a few campaigns of like, “this isn’t ready until we can open every single door at Boston Dynamics!” But every single door at Boston Dynamics and at our test lab is a small fraction of all the doors in the world. So we're prepared to learn a lot this year.

When we see Spot open a door, or when it does those other manipulation behaviors in the launch video, how much of that is autonomous, how much is scripted, and to what extent is there a human in the loop?

All of the scenes where the robot does a pick, like the snow scene or the laundry scene, that is actually an almost fully integrated autonomous behavior that has a bit of a script wrapped around it. We trained a detector for an object, and the robot is identifying that object in the environment, picking it, and putting it in the bin all autonomously. The scripted part of that is telling the robot to perform a series of picks.

One of the things that we’re excited about, and that roboticists have been excited about going back probably all the way to the DRC, is semi-autonomous manipulation. And so we have modes built into the interface where if you see an object that you want the robot to grab, all you have to do is tap that object on the screen, and the robot will walk up to it, use the depth camera in its gripper to capture a depth map, and plan a grasp on its own in real time. That’s all built-in, too.

The jump rope—robots don’t just go and jump rope on their own. We scripted an arm motion to move the rope, and wrote a script using our API to coordinate all three robots. Drawing “Boston Dynamics” in chalk in our parking lot was scripted also. One of our engineers wrote a really cool G-code interpreter that vectorizes graphics so that Spot can draw them.

So for an end user, if you wanted Spot to autonomously flip some switches for you, you’d just have to train Spot on your switches, and then Spot could autonomously perform the task?

There are a couple of ways that task could break down depending on how you’re interfacing with the robot. If you’re a tablet user, you’d probably just identify the switch yourself on the tablet’s screen, and the robot will figure out the grasp, and grasp it. Then you’ll enter a constrained manipulation mode on the tablet, and the robot will be able to actuate the switch. But the robot will take care of the complicated controls aspects, like figuring out how hard it has to pull, the center of rotation of the switch, and so on.

The video of Spot digging was pretty cool—how did that work?

That’s mostly a scripted behavior. There are some really interesting control systems topics in there, like how you’d actually do the right kinds of force control while you insert the trowel into the dirt, and how to maintain robot stability while you do it. The higher level task of how to make a good hole in the dirt—that’s scripted. But the part of the problem that’s actually digging, you need the right control system to actually do that, or you’ll dig your trowel into the ground and flip your robot over.

The last time we saw Boston Dynamics robots flipping switches and turning valves I think might have been during the DRC in 2015, when they had expert robot operators with control over every degree of freedom. How are things different now with Spot, and will non-experts in the commercial space really be able to get the robot to do useful tasks?

A lot of the things, like “pick the stuff up in the room,” or ‘turn that switch,” can all be done by a lightly trained operator using just the tablet interface. If you want to actually command all of Spot’s arm degrees of freedom, you can do that— not through the tablet, but the API does expose all of it. That’s actually a notable difference from the base robot; we’ve never opened up the part of the API that lets you command individual leg degrees of freedom, because we don’t think it’s productive for someone to do that. The arm is a little bit different. There are a lot of smart people working on arm motion planning algorithms, and maybe you want to plan your arm trajectory in a super precise way and then do a DRC-style interface where you click to approve it. You can do all that through the API if you want, but fundamentally, it’s also user friendly. It follows our general API design philosophy of giving you the highest level pieces of the toolbox that will enable you to solve a complex problem that we haven't thought of.

Looking back on it now, it’s really cool to see, after so many years, robots do the stuff that Gill Pratt was excited about kicking off with the DRC. And now it’s just a thing you can buy.

Is Spot’s arm safe?

You should follow the same safety rules that you’d follow when working with Spot normally, and that’s that you shouldn’t get within two meters of the robot when it’s powered on. Spot is not a cobot. You shouldn’t hug it. Fundamentally, the places where the robot is the most valuable are places where people don’t want to be, or shouldn’t be.

We’ve seen how people reacted to earlier videos of Spot using its arm—can you help us set some reasonable expectations for what this means for Spot?

You know, it gets right back to the normal assumptions about our robots that people make that aren’t quite reality. All of this manipulation work we’re doing— the robot’s really acting as a tool. Even if it’s an autonomous behavior, it’s a tool. The robot is digging a hole because it’s got a set of instructions that say “apply this much force over this much distance here, here, and here.”

It’s not digging a hole and planting a tree because it loves trees, as much as I’d love to build a robot that works like that.

Photo: Boston Dynamics

There isn’t too much to say about the dock, except that it’s a requirement for making Spot long-term autonomous. The uncomfortable looking charging contacts that Spot impales itself on also include hardwired network connectivity, which is important because Spot often comes back home with a huge amount of data that all needs to be offloaded and processed. Docking and undocking are autonomous— as soon as the robot sees the fiducial markers on the dock, auto docking is enabled and it takes one click to settle the robot down.

During a brief remote demo, we also learned some other interesting things about Spot’s updated remote interface. It’s very latency tolerant, since you don’t have to drive the robot directly (although you can if you want to). Click a point on the camera view and Spot will move there autonomously while avoiding obstacles, meaning that even if you’re dealing with seconds of lag, the robot will continue making safe progress. This will be especially important if (when?) Spot starts exploring the Moon.

The remote interface also has an option to adjust how close Spot can get to obstacles, or to turn the obstacle avoidance off altogether. The latter functionality is useful if Spot sees something as an obstacle that really isn’t, like a curtain, while the former is useful if the robot is operating in an environment where it needs to give an especially wide berth to objects that could be dangerous to run into. “The robot’s not perfect—robots will never be perfect,” Jackowski reminds us, which is something we really (seriously) appreciate hearing from folks working on powerful, dynamic robots. “No matter how good the robot is, you should always de-risk as much as possible.”

Another part of that de-risking is having the user let Spot know when it’s about to go up or down some stairs by putting into “Stair Mode” with a toggle switch in the remote interface. Stairs are still a challenge for Spot, and Stair Mode slows the robot down and encourages it to pitch its body more aggressively to get a better view of the stairs. You’re encouraged to use stair mode, and also encouraged to send Spot up and down stairs with its “head” pointing up the stairs both ways, but these are not requirements for stair navigation— if you want to, you can send Spot down stairs head first without putting it in stair mode. Jackowski says that eventually, Spot will detect stairways by itself even when not in stair mode and adjust itself accordingly, but for now, that de-risking is solidly in the hands of the user.

Spot’s sensor payload, which is what we were trying out for the demo, provided a great opportunity for us to hear Spot STOMP STOMP STOMPING all over the place, which was also an opportunity for us to ask Jackowski why they can’t make Spot a little quieter. “It’s advantageous for Spot to step a little bit hard for the same reason it’s advantageous for you to step a little bit hard if you’re walking around blindfolded—that reason is that it really lets you know where the ground is, particularly when you’re not sure what to expect.” He adds, “It’s all in the name of robustness— the robot might be a little louder, but it’s a little more sure of its footing.”

Boston Dynamics isn’t yet ready to disclose the price of an arm-equipped Spot, but if you’re a potential customer, now is the time to contact the Boston Dynamics sales team to ask them about it. As a reminder, the base model of Spot costs US $74,500, with extra sensing or compute adding a substantial premium on top of that.

There will be a livestream launch event taking place at 11am ET today, during which Boston Dynamics’ CEO Robert Playter, VP of Marketing Michael Perry, and other folks from Boston Dynamics will make presentations on this new stuff. It’ll be live at this link, or you can watch it below. Continue reading →

Posted in Human Robots

#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 →

Posted in Human Robots