Tag Archives: five

#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

#438012 Video Friday: These Robots Have Made 1 ...

Posted on January 29, 2021 by Android

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

HRI 2021 – March 8-11, 2021 – [Online Conference]
RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
Let us know if you have suggestions for next week, and enjoy today's videos.

We're proud to announce Starship Delivery Robots have now completed 1,000,000 autonomous deliveries around the world. We were unsure where the one millionth delivery was going to take place, as there are around 15-20 service areas open globally, all with robots doing deliveries every minute. In the end it took place at Bowling Green, Ohio, to a student called Annika Keeton who is a freshman studying pre-health Biology at BGSU. Annika is now part of Starship’s history!

[ Starship ]

I adore this little DIY walking robot- with modular feet and little dials to let you easily adjust the walking parameters, it's an affordable kit that's way more nuanced than most.

It's called Bakiwi, and it costs €95. A squee cover made from feathers or fur is an extra €17. Here's a more serious look at what it can do:

[ Bakiwi ]

Thanks Oswald!

Savva Morozov, an AeroAstro junior, works on autonomous navigation for the MIT mini cheetah robot and reflects on the value of a crowded Infinite Corridor.

[ MIT ]

The world's most advanced haptic feedback gloves just got a huge upgrade! HaptX Gloves DK2 achieves a level of realism that other haptic devices can't match. Whether you’re training your workforce, designing a new product, or controlling robots from a distance, HaptX Gloves make it feel real.

They're the only gloves with true-contact haptics, with patented technology that displace your skin the same way a real object would. With 133 points of tactile feedback per hand, for full palm and fingertip coverage. HaptX Gloves DK2 feature the industry's most powerful force feedback, ~2X the strength of other force feedback gloves. They're also the most accurate motion tracking gloves, with 30 tracked degrees of freedom, sub-millimeter precision, no perceivable latency, and no occlusion.

[ HaptX ]

Yardroid is an outdoor robot “guided by computer vision and artificial intelligence” that seems like it can do almost everything.

These are a lot of autonomous capabilities, but so far, we've only seen the video. So, best not to get too excited until we know more about how it works.

[ Yardroid ]

Thanks Dan!

Since as far as we know, Pepper can't spread COVID, it had a busy year.

I somehow missed seeing that chimpanzee magic show, but here it is:

[ Simon Pierro ] via [ SoftBank Robotics ]

In spite of the pandemic, Professor Hod Lipson’s Robotics Studio persevered and even thrived— learning to work on global teams, to develop protocols for sharing blueprints and code, and to test, evaluate, and refine their designs remotely. Equipped with a 3D printer and a kit of electronics prototyping equipment, our students engineered bipedal robots that were conceptualized, fabricated, programmed, and endlessly iterated around the globe in bedrooms, kitchens, backyards, and any other makeshift laboratory you can imagine.

[ Hod Lipson ]

Thanks Fan!

We all know how much quadrupeds love ice!

[ Ghost Robotics ]

We took the opportunity of the last storm to put the Warthog in the snow of Université Laval. Enjoy!

[ Norlab ]

They've got a long way to go, but autonomous indoor firefighting drones seem like a fantastic idea.

[ CTU ]

Individual manipulators are limited by their vertical total load capacity. This places a fundamental limit on the weight of loads that a single manipulator can move. Cooperative manipulation with two arms has the potential to increase the net weight capacity of the overall system. However, it is critical that proper load sharing takes place between the two arms. In this work, we outline a method that utilizes mechanical intelligence in the form of a whiffletree.

And your word of the day is whiffletree, which is “a mechanism to distribute force evenly through linkages.”

[ DART Lab ]

Thanks Raymond!

Some highlights of robotic projects at FZI in 2020, all using ROS.

[ FZI ]

Thanks Fan!

iRobot CEO Colin Angle threatens my job by sharing some cool robots.

[ iRobot ]

A fascinating new talk from Henry Evans on robotic caregivers.

[ HRL ]

The ANA Avatar XPRIZE semifinals selection submission for Team AVATRINA. The setting is a mock clinic, with the patient sitting on a wheelchair and nurse having completed an initial intake. Avatar enters the room controlled by operator (Doctor). A rolling tray table with medical supplies (stethoscope, pulse oximeter, digital thermometer, oxygen mask, oxygen tube) is by the patient’s side. Demonstrates head tracking, stereo vision, fine manipulation, bimanual manipulation, safe impedance control, and navigation.

[ Team AVATRINA ]

This five year old talk from Mikell Taylor, who wrote for us a while back and is now at Amazon Robotics, is entitled “Nobody Cares About Your Robot.” For better or worse, it really doesn't sound like it was written five years ago.

Robotics for the consumer market – Mikell Taylor from Scott Handsaker on Vimeo.

[ Mikell Taylor ]

Fall River Community Media presents this wonderful guy talking about his love of antique robot toys.

If you enjoy this kind of slow media, Fall River also has weekly Hot Dogs Cool Cats adoption profiles that are super relaxing to watch.

[ YouTube ] Continue reading →

Posted in Human Robots

#437872 AlphaFold Proves That AI Can Crack ...

Posted on December 7, 2020 by Android

Any successful implementation of artificial intelligence hinges on asking the right questions in the right way. That’s what the British AI company DeepMind (a subsidiary of Alphabet) accomplished when it used its neural network to tackle one of biology’s grand challenges, the protein-folding problem. Its neural net, known as AlphaFold, was able to predict the 3D structures of proteins based on their amino acid sequences with unprecedented accuracy.

AlphaFold’s predictions at the 14th Critical Assessment of protein Structure Prediction (CASP14) were accurate to within an atom’s width for most of the proteins. The competition consisted of blindly predicting the structure of proteins that have only recently been experimentally determined—with some still awaiting determination.

Called the building blocks of life, proteins consist of 20 different amino acids in various combinations and sequences. A protein's biological function is tied to its 3D structure. Therefore, knowledge of the final folded shape is essential to understanding how a specific protein works—such as how they interact with other biomolecules, how they may be controlled or modified, and so on. “Being able to predict structure from sequence is the first real step towards protein design,” says Janet M. Thornton, director emeritus of the European Bioinformatics Institute. It also has enormous benefits in understanding disease-causing pathogens. For instance, at the moment only about 18 of the 26 proteins in the SARS-CoV-2 virus are known.

Predicting a protein’s 3D structure is a computational nightmare. In 1969 Cyrus Levinthal estimated that there are 10300 possible conformational combinations for a single protein, which would take longer than the age of the known universe to evaluate by brute force calculation. AlphaFold can do it in a few days.

As scientific breakthroughs go, AlphaFold’s discovery is right up there with the likes of James Watson and Francis Crick’s DNA double-helix model, or, more recently, Jennifer Doudna and Emmanuelle Charpentier’s CRISPR-Cas9 genome editing technique.

How did a team that just a few years ago was teaching an AI to master a 3,000-year-old game end up training one to answer a question plaguing biologists for five decades? That, says Briana Brownell, data scientist and founder of the AI company PureStrategy, is the beauty of artificial intelligence: The same kind of algorithm can be used for very different things.

“Whenever you have a problem that you want to solve with AI,” she says, “you need to figure out how to get the right data into the model—and then the right sort of output that you can translate back into the real world.”

DeepMind’s success, she says, wasn’t so much a function of picking the right neural nets but rather “how they set up the problem in a sophisticated enough way that the neural network-based modeling [could] actually answer the question.”

AlphaFold showed promise in 2018, when DeepMind introduced a previous iteration of their AI at CASP13, achieving the highest accuracy among all participants. The team had trained its to model target shapes from scratch, without using previously solved proteins as templates.

For 2020 they deployed new deep learning architectures into the AI, using an attention-based model that was trained end-to-end. Attention in a deep learning network refers to a component that manages and quantifies the interdependence between the input and output elements, as well as between the input elements themselves.

The system was trained on public datasets of the approximately 170,000 known experimental protein structures in addition to databases with protein sequences of unknown structures.

“If you look at the difference between their entry two years ago and this one, the structure of the AI system was different,” says Brownell. “This time, they’ve figured out how to translate the real world into data … [and] created an output that could be translated back into the real world.”

Like any AI system, AlphaFold may need to contend with biases in the training data. For instance, Brownell says, AlphaFold is using available information about protein structure that has been measured in other ways. However, there are also many proteins with as yet unknown 3D structures. Therefore, she says, a bias could conceivably creep in toward those kinds of proteins that we have more structural data for.

Thornton says it’s difficult to predict how long it will take for AlphaFold’s breakthrough to translate into real-world applications.

“We only have experimental structures for about 10 per cent of the 20,000 proteins [in] the human body,” she says. “A powerful AI model could unveil the structures of the other 90 per cent.”

Apart from increasing our understanding of human biology and health, she adds, “it is the first real step toward… building proteins that fulfill a specific function. From protein therapeutics to biofuels or enzymes that eat plastic, the possibilities are endless.” Continue reading →

Posted in Human Robots

#437869 Video Friday: Japan’s Gundam Robot ...

Posted on December 5, 2020 by Android

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

ACRA 2020 – December 8-10, 2020 – [Online]
Let us know if you have suggestions for next week, and enjoy today’s videos.

Another BIG step for Japan’s Gundam project.

[ Gundam Factory ]

We present an interactive design system that allows users to create sculpting styles and fabricate clay models using a standard 6-axis robot arm. Given a general mesh as input, the user iteratively selects sub-areas of the mesh through decomposition and embeds the design expression into an initial set of toolpaths by modifying key parameters that affect the visual appearance of the sculpted surface finish. We demonstrate the versatility of our approach by designing and fabricating different sculpting styles over a wide range of clay models.

[ Disney Research ]

China’s Chang’e-5 completed the drilling, sampling and sealing of lunar soil at 04:53 BJT on Wednesday, marking the first automatic sampling on the Moon, the China National Space Administration (CNSA) announced Wednesday.

[ CCTV ]

Red Hat’s been putting together an excellent documentary on Willow Garage and ROS, and all five parts have just been released. We posted Part 1 a little while ago, so here’s Part 2 and Part 3.

Parts 4 and 5 are at the link below!

[ Red Hat ]

Congratulations to ANYbotics on a well-deserved raise!

ANYbotics has origins in the Robotic Systems Lab at ETH Zurich, and ANYmal’s heritage can be traced back at least as far as StarlETH, which we first met at ICRA 2013.

[ ANYbotics ]

Most conventional robots are working with 0.05-0.1mm accuracy. Such accuracy requires high-end components like low-backlash gears, high-resolution encoders, complicated CNC parts, powerful motor drives, etc. Those in combination end up an expensive solution, which is either unaffordable or unnecessary for many applications. As a result, we found the Apicoo Robotics to provide our customers solutions with a much lower cost and higher stability.

[ Apicoo Robotics ]

The Skydio 2 is an incredible drone that can take incredible footage fully autonomously, but it definitely helps if you do incredible things in incredible places.

[ Skydio ]

Jueying is the first domestic sensitive quadruped robot for industry applications and scenarios. It can coordinate (replace) humans to reach any place that can be reached. It has superior environmental adaptability, excellent dynamic balance capabilities and precise Environmental perception capabilities. By carrying functional modules for different application scenarios in the safe load area, the mobile superiority of the quadruped robot can be organically integrated with the commercialization of functional modules, providing smart factories, smart parks, scene display and public safety application solutions.

[ DeepRobotics ]

We have developed semi-autonomous quadruped robot, called LASER-D (Legged-Agile-Smart-Efficient Robot for Disinfection) for performing disinfection in cluttered environments. The robot is equipped with a spray-based disinfection system and leverages the body motion to controlling the spray action without the need for an extra stabilization mechanism. The system includes an image processing capability to verify disinfected regions with high accuracy. This system allows the robot to successfully carry out effective disinfection tasks while safely traversing through cluttered environments, climb stairs/slopes, and navigate on slippery surfaces.

[ USC Viterbi ]

We propose the “multi-vision hand”, in which a number of small high-speed cameras are mounted on the robot hand of a common 7 degrees-of-freedom robot. Also, we propose visual-servoing control by using a multi-vision system that combines the multi-vision hand and external fixed high-speed cameras. The target task was ball catching motion, which requires high-speed operation. In the proposed catching control, the catch position of the ball, which is estimated by the external fixed high-speed cameras, is corrected by the multi-vision hand in real-time.

More details available through IROS on-demand.

[ Namiki Laboratory ]

Shunichi Kurumaya wrote in to share his work on PneuFinger, a pneumatically actuated compliant robotic gripping system.

[ Nakamura Lab ]

Thanks Shunichi!

Motivated by insights into the human teaching process, we introduce a method for incorporating unstructured natural language into imitation learning. At training time, the expert can provide demonstrations along with verbal descriptions in order to describe the underlying intent, e.g., “Go to the large green bowl’’. The training process, then, interrelates the different modalities to encode the correlations between language, perception, and motion. The resulting language-conditioned visuomotor policies can be conditioned at run time on new human commands and instructions, which allows for more fine-grained control over the trained policies while also reducing situational ambiguity.

[ ASU ]

Thanks Heni!

Gita is on sale for the holidays for only $2,000.

[ Gita ]

This video introduces a computational approach for routing thin artificial muscle actuators through hyperelastic soft robots, in order to achieve a desired deformation behavior. Provided with a robot design, and a set of example deformations, we continuously co-optimize the routing of actuators, and their actuation, to approximate example deformations as closely as possible.

[ Disney Research ]

Researchers and mountain rescuers in Switzerland are making huge progress in the field of autonomous drones as the technology becomes more in-demand for global search-and-rescue operations.

[ SWI ]

This short clip of the Ghost Robotics V60 features an interesting, if awkward looking, righting behavior at the end.

[ Ghost Robotics ]

Europe’s Rosalind Franklin ExoMars rover has a younger ’sibling’, ExoMy. The blueprints and software for this mini-version of the full-size Mars explorer are available for free so that anyone can 3D print, assemble and program their own ExoMy.

[ ESA ]

The holiday season is here, and with the added impact of Covid-19 consumer demand is at an all-time high. Berkshire Grey is the partner that today’s leading organizations turn to when it comes to fulfillment automation.

[ Berkshire Grey ]

Until very recently, the vast majority of studies and reports on the use of cargo drones for public health were almost exclusively focused on the technology. The driving interest from was on the range that these drones could travel, how much they could carry and how they worked. Little to no attention was placed on the human side of these projects. Community perception, community engagement, consent and stakeholder feedback were rarely if ever addressed. This webinar presents the findings from a very recent study that finally sheds some light on the human side of drone delivery projects.

[ WeRobotics ] Continue reading →

Posted in Human Robots