Tag Archives: cameras

#439632 Intel Will Keep Selling RealSense Stereo ...

On Tuesday, CRN reported that Intel will be shutting down its RealSense division, which creates 3D vision systems used extensively in robotics. We confirmed the news with Intel directly on Wednesday, and Intel provided us with the following statement:
We are winding down our RealSense business and transitioning our computer vision talent, technology and products to focus on advancing innovative technologies that better support our core businesses and IDM 2.0 strategy. We will continue to meet our commitments to our current customers and are working with our employees and customers to ensure a smooth transition.However, after speaking with some of our industry sources to try and get a better sense of what happened, we learned that what's actually going on might be more nuanced. And as it turns out, it is: Intel will continue to provide RealSense stereo cameras to people who want them for now, although long term, things don't look good.
Intel's “RealSense business” encompasses a variety of different products. There's stereo depth, which includes the D415, D435, and D455 camera systems—these are what roboticists often use for 3D sensing. There's also lidar in the form of the L515 and associated software products, as well as biometric identification, which uses the F455 depth sensor, and a series of tracking and coded light cameras.
Intel has just confirmed with us that everything but the stereo cameras has been end of life'd. Here's the statement:
Intel has decided to wind down the RealSense business and is announcing the EOL of LiDAR, Facial Authentication and Tracking product lines this month. Intel will continue to provide select Stereo products to its current distribution customers. Hmm. The very careful wording here suggests some things to me, none of them good. The “RealSense business” is still being wound down, and while Intel will “continue to provide” RealSense cameras to customers, my interpretation is that they're still mostly doing what they said in their first release, which is moving their focus and talent elsewhere. So, no more development of new RealSense products, no more community engagement, and probably a minimal amount of support. If you want to buy a RealSense camera from a distributor, great, go ahead and do that, but I wouldn't look for much else. Also, “continue to provide” doesn't necessarily mean “continue to manufacture.” It could be that Intel has a big pile of cameras that they need to get rid of, and that once they're gone, that'll be the end of RealSense.
CRN managed to speak with Intel CEO Pat Gelsinger on Tuesday, and Gelsinger had this to add about the RealSense business:
“Hey, there's some good assets that we can harvest, but it doesn't fit one of those six business units that I've laid out.”
Oof.
We've asked Intel for additional detail, and we'll update this post if we hear anything more.
Sadly, many in the robotics community seemed unsurprised at the initial news about RealSense shutting down, which I guess makes sense, seeing as robotics has been burned in this way before—namely, with Microsoft's decision to discontinue the Kinect sensor (among other examples). What seemed different with RealSense was the extent to which Intel appeared to be interested in engaging with the robotics community and promoting RealSense to roboticists in a way that Microsoft never did with Kinect.
But even though it turns out that RealSense is still (technically) available, these statements over the last few days have created the feeling of a big company with other priorities, a company for whom robotics is a small enough market that it just doesn't really matter. I don't know if this is the reality over at Intel, but it's how things feel right now. My guess is that even roboticists who have been very happy with Intel will begin looking for alternatives.

The best and worst thing about RealSense could be that it's been just so darn ideal for robotics. Intel had the resources to make sensors with excellent performance and sell them for relatively cheap, and they've done exactly that. But in doing so, they've made it more difficult for alternative hardware to get a good foothold in the market, because for most people, RealSense is just the simple and affordable answer to stereo depth sensing. Maybe now, the other folks working on similar sensors (and there are a lot of companies doing very cool stuff) will be able to get a little more traction from researchers and companies who have abruptly been made aware of the need to diversify.
Even though it may not now be strictly necessary, within the next few weeks, we hope to take a look at other stereo depth sensing options for research and commercial robotics to get a better sense of what's out there. Continue reading

Posted in Human Robots

#439110 Robotic Exoskeletons Could One Day Walk ...

Engineers, using artificial intelligence and wearable cameras, now aim to help robotic exoskeletons walk by themselves.

Increasingly, researchers around the world are developing lower-body exoskeletons to help people walk. These are essentially walking robots users can strap to their legs to help them move.

One problem with such exoskeletons: They often depend on manual controls to switch from one mode of locomotion to another, such as from sitting to standing, or standing to walking, or walking on the ground to walking up or down stairs. Relying on joysticks or smartphone apps every time you want to switch the way you want to move can prove awkward and mentally taxing, says Brokoslaw Laschowski, a robotics researcher at the University of Waterloo in Canada.

Scientists are working on automated ways to help exoskeletons recognize when to switch locomotion modes — for instance, using sensors attached to legs that can detect bioelectric signals sent from your brain to your muscles telling them to move. However, this approach comes with a number of challenges, such as how how skin conductivity can change as a person’s skin gets sweatier or dries off.

Now several research groups are experimenting with a new approach: fitting exoskeleton users with wearable cameras to provide the machines with vision data that will let them operate autonomously. Artificial intelligence (AI) software can analyze this data to recognize stairs, doors, and other features of the surrounding environment and calculate how best to respond.

Laschowski leads the ExoNet project, the first open-source database of high-resolution wearable camera images of human locomotion scenarios. It holds more than 5.6 million images of indoor and outdoor real-world walking environments. The team used this data to train deep-learning algorithms; their convolutional neural networks can already automatically recognize different walking environments with 73 percent accuracy “despite the large variance in different surfaces and objects sensed by the wearable camera,” Laschowski notes.

According to Laschowski, a potential limitation of their work their reliance on conventional 2-D images, whereas depth cameras could also capture potentially useful distance data. He and his collaborators ultimately chose not to rely on depth cameras for a number of reasons, including the fact that the accuracy of depth measurements typically degrades in outdoor lighting and with increasing distance, he says.

In similar work, researchers in North Carolina had volunteers with cameras either mounted on their eyeglasses or strapped onto their knees walk through a variety of indoor and outdoor settings to capture the kind of image data exoskeletons might use to see the world around them. The aim? “To automate motion,” says Edgar Lobaton an electrical engineering researcher at North Carolina State University. He says they are focusing on how AI software might reduce uncertainty due to factors such as motion blur or overexposed images “to ensure safe operation. We want to ensure that we can really rely on the vision and AI portion before integrating it into the hardware.”

In the future, Laschowski and his colleagues will focus on improving the accuracy of their environmental analysis software with low computational and memory storage requirements, which are important for onboard, real-time operations on robotic exoskeletons. Lobaton and his team also seek to account for uncertainty introduced into their visual systems by movements .

Ultimately, the ExoNet researchers want to explore how AI software can transmit commands to exoskeletons so they can perform tasks such as climbing stairs or avoiding obstacles based on a system’s analysis of a user's current movements and the upcoming terrain. With autonomous cars as inspiration, they are seeking to develop autonomous exoskeletons that can handle the walking task without human input, Laschowski says.

However, Laschowski adds, “User safety is of the utmost importance, especially considering that we're working with individuals with mobility impairments,” resulting perhaps from advanced age or physical disabilities.
“The exoskeleton user will always have the ability to override the system should the classification algorithm or controller make a wrong decision.” Continue reading

Posted in Human Robots

#439066 Video Friday: Festo’s BionicSwift

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!):

RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
DARPA SubT Finals – September 21-23, 2021 – Louisville, KY, USA
WeRobot 2021 – September 23-25, 2021 – Coral Gables, FL, USA
Let us know if you have suggestions for next week, and enjoy today's videos.

Festo's Bionic Learning Network for 2021 presents a flock of BionicSwifts.

To execute the flight maneuvers as true to life as possible, the wings are modeled on the plumage of birds. The individual lamellae are made of an ultralight, flexible but very robust foam and lie on top of each other like shingles. Connected to a carbon quill, they are attached to the actual hand and arm wings as in the natural model.

During the wing upstroke, the individual lamellae fan out so that air can flow through the wing. This means that the birds need less force to pull the wing up. During the downstroke, the lamellae close up so that the birds can generate more power to fly. Due to this close-to-nature replica of the wings, the BionicSwifts have a better flight profile than previous wing-beating drives.

[ Festo ]

While we've seen a wide variety of COVID-motivated disinfecting robots, they're usually using either ultraviolet light or a chemical fog. This isn't the way that humans clean—we wipe stuff down, which gets rid of surface dirt and disinfects at the same time. Fraunhofer has been working on a mobile manipulator that can clean in the same ways that we do.

It's quite the technical challenge, but it has the potential to be both more efficient and more effective.

[ Fraunhofer ]

In recent years, robots have gained artificial vision, touch, and even smell. “Researchers have been giving robots human-like perception,” says MIT Associate Professor Fadel Adib. In a new paper, Adib’s team is pushing the technology a step further. “We’re trying to give robots superhuman perception,” he says. The researchers have developed a robot that uses radio waves, which can pass through walls, to sense occluded objects. The robot, called RF-Grasp, combines this powerful sensing with more traditional computer vision to locate and grasp items that might otherwise be blocked from view.

[ MIT ]

Ingenuity is now scheduled to fly on April 11.

[ JPL ]

The legendary Zenta is back after a two year YouTube hiatus with “a kind of freaky furry hexapod bunny creature.”

[ Zenta ]

It is with great pride and excitement that the South Australia Police announce a new expansion to their kennel by introducing three new Police Dog (PD) recruits. These dogs have been purposely targeted to bring a whole new range of dog operational capabilities known as the ‘small area urban search and guided evacuation’ dogs. Police have been working closely with specialist vets and dog trainers to ascertain if the lightweight dogs could be transported safely by drones and released into hard-to-access areas where at the moment the larger PDs just simply cannot get in due to their size.

[ SA Police ]

SoftBank may not have Spot cheerleading robots for their baseball team anymore, but they've more than made up for it with a full century of Peppers. And one dude doing the robot.

[ SoftBank ]

MAB Robotics is a Polish company developing walking robots for inspection, and here's a prototype they've been working on.

[ MAB Robotics ]

Thanks Jakub!

DoraNose: Smell your way to a better tomorrow.

[ Dorabot ]

Our robots need to learn how to cope with their new neighbors, and we have just the solution for this, the egg detector! Using cutting-edge AI, it provides incredible precision in detecting a vast variety of eggs. We have deployed this new feature on Boston Dynamics Spot, one of our fleet's robots. It can now detect eggs with its cameras and avoid them on his autonomous missions.

[ Energy Robotics ]

When dropping a squishy robot from an airplane 1,000 feet up, make sure that you land as close to people's cars as you can.

Now do it from orbit!

[ Squishy Robotics ]

An autonomous robot that is able to physically guide humans through narrow and cluttered spaces could be a big boon to the visually-impaired. Most prior robotic guiding systems are based on wheeled platforms with large bases with actuated rigid guiding canes. The large bases and the actuated arms limit these prior approaches from operating in narrow and cluttered environments. We propose a method that introduces a quadrupedal robot with a leash to enable the robot-guiding-human system to change its intrinsic dimension (by letting the leash go slack) in order to fit into narrow spaces.

[ Hybrid Robotics ]

How to prove that your drone is waterproof.

[ UNL ]

Well this ought to be pretty good once it gets out of simulation.

[ Hybrid Robotics ]

MIDAS is Aurora’s AI-enabled, multi-rotor sUAV outfitted with optical sensors and a customized payload that can defeat multiple small UAVs per flight with low-collateral effects.

[ Aurora ]

The robots​ of the DFKI have the advantage of being able to reach extreme environments: they can be used for decontamination purposes in high-risk areas or inspect and maintain underwater​ structures, for which they are tested in the North Sea near Heligoland​.

[ DFKI ]

After years of trying, 60 Minutes cameras finally get a peek inside the workshop at Boston Dynamics, where robots move in ways once only thought possible in movies. Anderson Cooper reports.

[ 60 Minutes ]

In 2007, Noel Sharky stated that “we are sleepwalking into a brave new world where robots decide who, where and when to kill.” Since then thousands of AI and robotics researchers have joined his calls to regulate “killer robots.” But sometime this year, Turkey will deploy fully autonomous home-built kamikaze drones on its border with Syria. What are the ethical choices we need to consider? Will we end up in an episode of Black Mirror? Or is the UN listening to calls and starting the process of regulating this space? Prof. Toby Walsh will discuss this important issue, consider where we are at and where we need to go.

[ ICRA 2020 ]

In the second session of HAI's spring conference, artists and technologists discussed how technology can enhance creativity, reimagine meaning, and support racial and social justice. The conference, called “Intelligence Augmentation: AI Empowering People to Solve Global Challenges,” took place on 25 March 2021.

[ Stanford HAI ]

This spring 2021 GRASP SFI comes from Monroe Kennedy III at Stanford University, on “Considerations for Human-Robot Collaboration.”

The field of robotics has evolved over the past few decades. We’ve seen robots progress from the automation of repetitive tasks in manufacturing to the autonomy of mobilizing in unstructured environments to the cooperation of swarm robots that are centralized or decentralized. These abilities have required advances in robotic hardware, modeling, and artificial intelligence. The next frontier is robots collaborating in complex tasks with human teammates, in environments traditionally configured for humans. While solutions to this challenge must utilize all the advances of robotics, the human element adds a unique aspect that must be addressed. Collaborating with a human teammate means that the robot must have a contextual understanding of the task as well as all participant’s roles. We will discuss what constitutes an effective teammate and how we can capture this behavior in a robotic collaborator.

[ UPenn ] Continue reading

Posted in Human Robots

#439055 Stretch Is Boston Dynamics’ Take on a ...

Today, Boston Dynamics is announcing Stretch, a mobile robot designed to autonomously move boxes around warehouses. At first glance, you might be wondering why the heck this is a Boston Dynamics robot at all, since the dynamic mobility that we associate with most of their platforms is notably absent. The combination of strength and speed in Stretch’s arm is something we haven’t seen before in a mobile robot, and it’s what makes this a unique and potentially exciting entry into the warehouse robotics space.

Useful mobile manipulation in any environment that’s not almost entirely structured is still a significant challenge in robotics, and it requires a very difficult combination of sensing, intelligence, and dynamic motion, all of which are classic Boston Dynamics. But also classic Boston Dynamics is building really cool platforms, and only later trying to figure out a way of making them commercially viable. So why Stretch, why boxes, why now, and (the real question) why not Handle? We talk with Boston Dynamics’ Vice President of Product Engineering Kevin Blankespoor to find out.

Stretch is very explicitly a box-handling mobile robot for relatively well structured warehouses. It’s in no way designed to be a generalist that many of Boston Dynamics’ other robots are. And to be fair, this is absolutely how to make a robot that’s practical and cost effective right out of the crate: Identify a task that is dull or dirty or dangerous for humans, design a robot to do that task safely and efficiently, and deploy it with the expectation that it’ll be really good at that task but not necessarily much else. This is a very different approach than a robot like Spot, where the platform came first and the practical applications came later—with Stretch, it’s all about that specific task in a specific environment.

There are already robotic solutions for truck unloading, palletizing, and depalletizing, but Stretch seems to be uniquely capable. For truck unloading, the highest performance systems that I’m aware of are monstrous things (here’s one example from Honeywell) that use a ton of custom hardware to just sort of ingest the cargo within a trailer all at once. In a highly structured and predictable warehouse, this sort of thing may pay off over the long term, but it’s going to be extremely expensive and not very versatile at all.

Palletizing and depalletizing robots are much more common in warehouses today. They’re almost always large industrial arms surrounded by a network of custom conveyor belts and whatnot, suffering from the same sorts of constraints as a truck unloader— very capable in some situations, but generally high cost and low flexibility.

Photo: Boston Dynamics

Stretch is probably not going to be able to compete with either of these types of dedicated systems when it comes to sheer speed, but it offers lots of other critical advantages: It’s fast and easy to deploy, easy to use, and adaptable to a variety of different tasks without costly infrastructure changes. It’s also very much not Handle, which was Boston Dynamics’ earlier (although not that much earlier) attempt at a box-handling robot for warehouses, and (let’s be honest here) a much more Boston Dynamics-y thing than Stretch seems to be. To learn more about why the answer is Stretch rather than Handle, and how Stretch will fit into the warehouse of the very near future, we spoke with Kevin Blankespoor, Boston Dynamics’ VP of Product Engineering and chief engineer for both Handle and Stretch.

IEEE Spectrum: Tell me about Stretch!

Kevin Blankespoor: Stretch is the first mobile robot that we’ve designed specifically for the warehouse. It’s all about moving boxes. Stretch is a flexible robot that can move throughout the warehouse and do different tasks. During a typical day in the life of Stretch in the future, it might spend the morning on the inbound side of the warehouse unloading boxes from trucks. It might spend the afternoon in the aisles of the warehouse building up pallets to go to retailers and e-commerce facilities, and it might spend the evening on the outbound side of the warehouse loading boxes into the trucks. So, it really goes to where the work is.

There are already other robots that include truck unloading robots, palletizing and depalletizing robots, and mobile bases with arms on them. What makes Boston Dynamics the right company to introduce a new robot in this space?

We definitely thought through this, because there are already autonomous mobile robots [AMRs] out there. Most of them, though, are more like pallet movers or tote movers—they don't have an arm, and most of them are really just about moving something from point A to point B without manipulation capability. We've seen some experiments where people put arms on AMRs, but nothing that's made it very far in the market. And so when we started looking at Stretch, we realized we really needed to make a custom robot, and that it was something we could do quickly.

“We got a lot of interest from people who wanted to put Atlas to work in the warehouse, but we knew that we could build a simpler robot to do some of those same tasks.”

Stretch is built with pieces from Spot and Atlas and that gave us a big head start. For example, if you look at Stretch’s vision system, it's 2D cameras, depth sensors, and software that allows it to do obstacle detection, box detection, and localization. Those are all the same sensors and software that we've been using for years on our legged robots. And if you look closely at Stretch’s wrist joints, they're actually the same as Spot’s hips. They use the same electric motors, the same gearboxes, the same sensors, and they even have the same closed-loop controller controlling the joints.

If you were to buy an existing industrial robot arm with this kind of performance, it would be about four times heavier than the arm we built, and it's really hard to make that into a mobile robot. A lot of this came from our leg technology because it’s so important for our leg designs to be lightweight for the robots to balance. We took that same strength to weight advantage that we have, and built it into this arm. We're able to rapidly piece together things from our other robots to get us out of the gate quickly, so even though this looks like a totally different robot, we think we have a good head start going into this market.

At what point did you decide to go with an arm on a statically stable base on Stretch, rather than something more, you know, dynamic-y?

Stretch looks really different than the robots that Boston Dynamics has done in the past. But you'd be surprised how much similarity there is between our legged robots and Stretch under the hood. Looking back, we actually got our start on moving boxes with Atlas, and at that point it was just research and development. We were really trying to do force control for box grasping. We were picking up heavy boxes and maintaining balance and working on those fundamentals. We released a video of that as our first next-gen Atlas video, and it was interesting. We got a lot of interest from people who wanted to put Atlas to work in the warehouse, but we knew that we could build a simpler robot to do some of those same tasks.

So at this point we actually came up with Handle. The intent of Handle was to do a couple things—one was, we thought we could build a simpler robot that had Atlas’ attributes. Handle has a small footprint so it can fit in tight spaces, but it can pick up heavy boxes. And in addition to that, we had always really wanted to combine wheels and legs. We’d been talking about doing that for a decade and so Handle was a chance for us to try it.

We built a couple versions of Handle, and the first one was really just a prototype to kind of explore the morphology. But the second one was more purpose-built for warehouse tasks, and we started building pallets with that one and it looked pretty good. And then we started doing truck unloading with Handle, which was the pivotal moment. Handle could do it, but it took too long. Every time Handle grasped a box, it would have to roll back and then get to a place where it could spin itself to face forward and place the box, and trucks are very tight for a robot this size, so there's not a lot of room to maneuver. We knew the whole time that there was a robot like Stretch that was another alternative, but that's really when it became clear that Stretch would have a lot of advantages, and we started working on it about a year ago.

Stretch is certainly impressive in a practical way, but I’ll admit to really hoping that something like Handle could have turned out to be a viable warehouse robot.

I love the Handle project as well, and I’m very passionate about that robot. And there was a stage before we built Stretch where we thought, “this would be pretty standard looking compared to Handle, is it going to capture enough of the Boston Dynamics secret sauce?” But when you actually dissect all the problems within Stretch that you have to tackle, there are a lot of cool robotics problems left in there—the vision system, the planning, the manipulation, the grasping of the boxes—it's a lot harder to solve than it looks, and we're excited that we're actually getting fairly far down that road now.

What happens to Handle now?

Stretch has really taken over our team as far as warehouse products go. Handle we still use occasionally as a research robot, but it’s not actively under development. Stretch is really Handle’s descendent. Handle’s not retired, exactly, but we’re just using it for things like the dance video.

There’s still potential to do cool stuff with Handle. I do think that combining wheels with legs is very cool, and largely unexplored compared to its potential. So I still think that you're gonna see versions of robots combining wheels and legs like Handle, and maybe a version of Handle in the future that does more of that. But because we're switching this thread from research into product, Stretch is really the main focus now.

How autonomous is Stretch?

Stretch is semi-autonomous, and that means it really needs to work with people to tap into its full potential. With truck unloading, for example, a person will drive Stretch into the back of the truck and then basically point Stretch in the right direction and say go. And from that point on, everything’s autonomous. Stretch has its vision system and its mobility and it can detect all the boxes, grasp all boxes, and move them onto a conveyor all autonomously. This is something that takes people hours to do manually, and Stretch can go all the way until it gets to the last box, and the truck is empty. There are some parts of the truck unloading task that do require people, like verifying that the truck is in the right place and opening the doors. But this takes a person just a few minutes, and then the robot can spend hours or as long as it takes to do its job autonomously.

There are also other tasks in the warehouse where the autonomy will increase in the future. After truck unloading, the second thing we’ll take on is order building, which will be more in the aisles of a warehouse. For that, Stretch will be navigating around the warehouse, finding the right pallet it needs to take a box from, and loading it onto a new pallet. This will be a different model with more autonomy; you’ll still have people involved to some degree, but the robot will have a higher percentage of the time where it can work independently.

What kinds of constraints is Stretch operating under? Do the boxes all have to be stacked neatly in the back of the truck, do they have to be the same size, the same color, etc?

“This will be a different model with more autonomy. You’ll still have people involved to some degree, but the robot will have a higher percentage of the time where it can work independently.”

If you think about manufacturing, where there's been automation for decades, you can go into a modern manufacturing facility and there are robot arms and conveyors and other machines. But if you look at the actual warehouse space, 90+ percent is manually operated, and that's because of what you just asked about— things that are less structured, where there’s more variety, and it's more challenging for a robot. But this is starting to change. This is really, really early days, and you’re going to be seeing a lot more robots in the warehouse space.

The warehouse robotics industry is going to grow a lot over the next decade, and a lot of that boils down to vision—the ability for robots to navigate and to understand what they’re seeing. Actually seeing boxes in real world scenarios is challenging, especially when there's a lot of variety. We've been testing our machine learning-based box detection system on Pick for a few years now, and it's gotten far enough that we know it’s one of the technical hurdles you need to overcome to succeed in the warehouse.

Can you compare the performance of Stretch to the performance of a human in a box-unloading task?

Stretch can move cases up to 50 pounds which is the OSHA limit for how much a single person's allowed to move. The peak case rate for Stretch is 800 cases per hour. You really need to keep up with the flow of goods throughout the warehouse, and 800 cases per hour should be enough for most applications. This is similar to a really good human; most humans are probably slower, and it’s hard for a human to sustain that rate, and one of the big issues with people doing this jobs is injury rates. Imagine moving really heavy boxes all day, and having to reach up high or bend down to get them—injuries are really common in this area. Truck unloading is one of the hardest jobs in a warehouse, and that’s one of the reasons we’re starting there with Stretch.

Is Stretch safe for humans to be around?

We looked at using collaborative robot arms for Stretch, but they don’t have the combination of strength and speed and reach to do this task. That’s partially just due to the laws of physics—if you want to move a 50lb box really fast, that’s a lot of energy there. So, Stretch does need to maintain separation from humans, but it’s pretty safe when it’s operating in the back of a truck.

In the middle of a warehouse, Stretch will have a couple different modes. When it's traveling around it'll be kind of like an AMR, and use a safety-rated lidar making sure that it slows down or stops as people get closer. If it's parked and the arm is moving, it'll do the same thing, monitoring anyone getting close and either slow down or stop.

How do you see Stretch interacting with other warehouse robots?

For building pallet orders, we can do that in a couple of different ways, and we’re experimenting with partners in the AMR space. So you might have an AMR that moves the pallet around and then rendezvous with Stretch, and Stretch does the manipulation part and moves boxes onto the pallet, and then the AMR scuttles off to the next rendezvous point where maybe a different Stretch meets it. We’re developing prototypes of that behavior now with a few partners. Another way to do it is Stretch can actually pull the pallet around itself and do both tasks. There are two fundamental things that happen in the warehouse: there's movement of goods, and there's manipulation of goods, and Stretch can do both.

You’re aware that Hello Robot has a mobile manipulator called Stretch, right?

Great minds think alike! We know Aaron [Edsinger] from the Google days; we all used to be in the same company, and he’s a great guy. We’re in very different applications and spaces, though— Aaron’s robot is going into research and maybe a little bit into the consumer space, while this robot is on a much bigger scale aimed at industrial applications, so I think there’s actually a lot of space between our robots, in terms of how they’ll be used.

Editor’s Note: We did check in with Aaron Edsinger at Hello Robot, and he sees things a little bit differently. “We're disappointed they chose our name for their robot,” Edsinger told us. “We're seriously concerned about it and considering our options.” We sincerely hope that Boston Dynamics and Hello Robot can come to an amicable solution on this.
What’s the timeline for commercial deployment of Stretch?

This is a prototype of the Stretch robot, and anytime we design a new robot, we always like to build a prototype as quickly as possible so we can figure out what works and what doesn't work. We did that with our bipeds and quadrupeds as well. So, we get an early look at what we need to iterate, because any time you build the first thing, it's not the right thing, and you always need to make changes to get to the final version. We've got about six of those Stretch prototypes operating now. In parallel, our hardware team is finishing up the design of the productized version of Stretch. That version of Stretch looks a lot like the prototype, but every component has been redesigned from the ground up to be manufacturable, to be reliable, and to be higher performance.

For the productized version of Stretch, we’ll build up the first units this summer, and then it’ll go on sale next year. So this is kind of a sneak peak into what the final product will be.

How much does it cost, and will you be selling Stretch, or offering it as a service?

We’re not quite ready to talk about cost yet, but it’ll be cost effective, and similar in cost to existing systems if you were to combine an industrial robot arm, custom gripper, and mobile base. We’re considering both selling and leasing as a service, but we’re not quite ready to narrow it down yet.

Photo: Boston Dynamics

As with all mobile manipulators, what Stretch can do long-term is constrained far more by software than by hardware. With a fast and powerful arm, a mobile base, a solid perception system, and 16 hours of battery life, you can imagine how different grippers could enable all kinds of different capabilities. But we’re getting ahead of ourselves, because it’s a long, long way from getting a prototype to work pretty well to getting robots into warehouses in a way that’s commercially viable long-term, even when the use case is as clear as it seems to be for Stretch.

Stretch also could signal a significant shift in focus for Boston Dynamics. While Blankespoor’s comments about Stretch leveraging Boston Dynamics’ expertise with robots like Spot and Atlas are well taken, Stretch is arguably the most traditional robot that the company has designed, and they’ve done so specifically to be able to sell robots into industry. This is what you do if you’re a robotics company who wants to make money by selling robots commercially, which (historically) has not been what Boston Dynamics is all about. Despite its bonkers valuation, Boston Dynamics ultimately needs to make money, and robots like Stretch are a good way to do it. With that in mind, I wouldn’t be surprised to see more robots like this from Boston Dynamics—robots that leverage the company’s unique technology, but that are designed to do commercially useful tasks in a somewhat less flashy way. And if this strategy keeps Boston Dynamics around (while funding some occasional creative craziness), then I’m all for it. Continue reading

Posted in Human Robots

#438807 Visible Touch: How Cameras Can Help ...

The dawn of the robot revolution is already here, and it is not the dystopian nightmare we imagined. Instead, it comes in the form of social robots: Autonomous robots in homes and schools, offices and public spaces, able to interact with humans and other robots in a socially acceptable, human-perceptible way to resolve tasks related to core human needs.

To design social robots that “understand” humans, robotics scientists are delving into the psychology of human communication. Researchers from Cornell University posit that embedding the sense of touch in social robots could teach them to detect physical interactions and gestures. They describe a way of doing so by relying not on touch but on vision.

A USB camera inside the robot captures shadows of hand gestures on the robot’s surface and classifies them with machine-learning software. They call this method ShadowSense, which they define as a modality between vision and touch, bringing “the high resolution and low cost of vision-sensing to the close-up sensory experience of touch.”

Touch-sensing in social or interactive robots is usually achieved with force sensors or capacitive sensors, says study co-author Guy Hoffman of the Sibley School of Mechanical and Aerospace Engineering at Cornell University. The drawback to his group’s approach has been that, even to achieve coarse spatial resolution, many sensors are needed in a small area.

However, working with non-rigid, inflatable robots, Hoffman and his co-researchers installed a consumer-grade USB camera to which they attached a fisheye lens for a wider field of vision.

“Given that the robot is already hollow, and has a soft and translucent skin, we could do touch interaction by looking at the shadows created by people touching the robot,” says Hoffman. They used deep neural networks to interpret the shadows. “And we were able to do it with very high accuracy,” he says. The robot was able to interpret six different gestures, including one- or two-handed touch, pointing, hugging and punching, with an accuracy of 87.5 to 96 percent, depending on the lighting.

This is not the first time that computer vision has been used for tactile sensing, though the scale and application of ShadowSense is unique. “Photography has been used for touch mainly in robotic grasping,” says Hoffman. By contrast, Hoffman and collaborators wanted to develop a sense that could be “felt” across the whole of the device.

The potential applications for ShadowSense include mobile robot guidance using touch, and interactive screens on soft robots. A third concerns privacy, especially in home-based social robots. “We have another paper currently under review that looks specifically at the ability to detect gestures that are further away [from the robot’s skin],” says Hoffman. This way, users would be able to cover their robot’s camera with a translucent material and still allow it to interpret actions and gestures from shadows. Thus, even though it’s prevented from capturing a high-resolution image of the user or their surrounding environment, using the right kind of training datasets, the robot can continue to monitor some kinds of non-tactile activities.

In its current iteration, Hoffman says, ShadowSense doesn’t do well in low-light conditions. Environmental noise, or shadows from surrounding objects, also interfere with image classification. Relying on one camera also means a single point of failure. “I think if this were to become a commercial product, we would probably [have to] work a little bit better on image detection,” says Hoffman.

As it was, the researchers used transfer learning—reusing a pre-trained deep-learning model in a new problem—for image analysis. “One of the problems with multi-layered neural networks is that you need a lot of training data to make accurate predictions,” says Hoffman. “Obviously, we don’t have millions of examples of people touching a hollow, inflatable robot. But we can use pre-trained networks trained on general images, which we have billions of, and we only retrain the last layers of the network using our own dataset.” Continue reading

Posted in Human Robots