Tag Archives: Simulation

#437940 How Boston Dynamics Taught Its Robots to ...

Posted on January 7, 2021 by Android

A week ago, Boston Dynamics posted a video of Atlas, Spot, and Handle dancing to “Do You Love Me.” It was, according to the video description, a way “to celebrate the start of what we hope will be a happier year.” As of today the video has been viewed nearly 24 million times, and the popularity is no surprise, considering the compelling mix of technical prowess and creativity on display.

Strictly speaking, the stuff going on in the video isn’t groundbreaking, in the sense that we’re not seeing any of the robots demonstrate fundamentally new capabilities, but that shouldn’t take away from how impressive it is—you’re seeing state-of-the-art in humanoid robotics, quadrupedal robotics, and whatever-the-heck-Handle-is robotics.

What is unique about this video from Boston Dynamics is the artistic component. We know that Atlas can do some practical tasks, and we know it can do some gymnastics and some parkour, but dancing is certainly something new. To learn more about what it took to make these dancing robots happen (and it’s much more complicated than it might seem), we spoke with Aaron Saunders, Boston Dynamics’ VP of Engineering.

Saunders started at Boston Dynamics in 2003, meaning that he’s been a fundamental part of a huge number of Boston Dynamics’ robots, even the ones you may have forgotten about. Remember LittleDog, for example? A team of two designed and built that adorable little quadruped, and Saunders was one of them.

While he’s been part of the Atlas project since the beginning (and had a hand in just about everything else that Boston Dynamics works on), Saunders has spent the last few years leading the Atlas team specifically, and he was kind enough to answer our questions about their dancing robots.

IEEE Spectrum: What’s your sense of how the Internet has been reacting to the video?

Aaron Saunders: We have different expectations for the videos that we make; this one was definitely anchored in fun for us. The response on YouTube was record-setting for us: We received hundreds of emails and calls with people expressing their enthusiasm, and also sharing their ideas for what we should do next, what about this song, what about this dance move, so that was really fun. My favorite reaction was one that I got from my 94-year-old grandma, who watched the video on YouTube and then sent a message through the family asking if I’d taught the robot those sweet moves. I think this video connected with a broader audience, because it mixed the old-school music with new technology.

We haven’t seen Atlas move like this before—can you talk about how you made it happen?

We started by working with dancers and a choreographer to create an initial concept for the dance by composing and assembling a routine. One of the challenges, and probably the core challenge for Atlas in particular, was adjusting human dance moves so that they could be performed on the robot. To do that, we used simulation to rapidly iterate through movement concepts while soliciting feedback from the choreographer to reach behaviors that Atlas had the strength and speed to execute. It was very iterative—they would literally dance out what they wanted us to do, and the engineers would look at the screen and go “that would be easy” or “that would be hard” or “that scares me.” And then we’d have a discussion, try different things in simulation, and make adjustments to find a compatible set of moves that we could execute on Atlas.

Throughout the project, the time frame for creating those new dance moves got shorter and shorter as we built tools, and as an example, eventually we were able to use that toolchain to create one of Atlas’ ballet moves in just one day, the day before we filmed, and it worked. So it’s not hand-scripted or hand-coded, it’s about having a pipeline that lets you take a diverse set of motions, that you can describe through a variety of different inputs, and push them through and onto the robot.

Image: Boston Dynamics

Were there some things that were particularly difficult to translate from human dancers to Atlas? Or, things that Atlas could do better than humans?

Some of the spinning turns in the ballet parts took more iterations to get to work, because they were the furthest from leaping and running and some of the other things that we have more experience with, so they challenged both the machine and the software in new ways. We definitely learned not to underestimate how flexible and strong dancers are—when you take elite athletes and you try to do what they do but with a robot, it’s a hard problem. It’s humbling. Fundamentally, I don’t think that Atlas has the range of motion or power that these athletes do, although we continue developing our robots towards that, because we believe that in order to broadly deploy these kinds of robots commercially, and eventually in a home, we think they need to have this level of performance.

One thing that robots are really good at is doing something over and over again the exact same way. So once we dialed in what we wanted to do, the robots could just do it again and again as we played with different camera angles.

I can understand how you could use human dancers to help you put together a routine with Atlas, but how did that work with Spot, and particularly with Handle?

I think the people we worked with actually had a lot of talent for thinking about motion, and thinking about how to express themselves through motion. And our robots do motion really well—they’re dynamic, they’re exciting, they balance. So I think what we found was that the dancers connected with the way the robots moved, and then shaped that into a story, and it didn’t matter whether there were two legs or four legs. When you don’t necessarily have a template of animal motion or human behavior, you just have to think a little harder about how to go about doing something, and that’s true for more pragmatic commercial behaviors as well.

“We used simulation to rapidly iterate through movement concepts while soliciting feedback from the choreographer to reach behaviors that Atlas had the strength and speed to execute. It was very iterative—they would literally dance out what they wanted us to do, and the engineers would look at the screen and go ‘that would be easy’ or ‘that would be hard’ or ‘that scares me.’”
—Aaron Saunders, Boston Dynamics

How does the experience that you get teaching robots to dance, or to do gymnastics or parkour, inform your approach to robotics for commercial applications?

We think that the skills inherent in dance and parkour, like agility, balance, and perception, are fundamental to a wide variety of robot applications. Maybe more importantly, finding that intersection between building a new robot capability and having fun has been Boston Dynamics’ recipe for robotics—it’s a great way to advance.

One good example is how when you push limits by asking your robots to do these dynamic motions over a period of several days, you learn a lot about the robustness of your hardware. Spot, through its productization, has become incredibly robust, and required almost no maintenance—it could just dance all day long once you taught it to. And the reason it’s so robust today is because of all those lessons we learned from previous things that may have just seemed weird and fun. You’ve got to go into uncharted territory to even know what you don’t know.

Image: Boston Dynamics

It’s often hard to tell from watching videos like these how much time it took to make things work the way you wanted them to, and how representative they are of the actual capabilities of the robots. Can you talk about that?

Let me try to answer in the context of this video, but I think the same is true for all of the videos that we post. We work hard to make something, and once it works, it works. For Atlas, most of the robot control existed from our previous work, like the work that we’ve done on parkour, which sent us down a path of using model predictive controllers that account for dynamics and balance. We used those to run on the robot a set of dance steps that we’d designed offline with the dancers and choreographer. So, a lot of time, months, we spent thinking about the dance and composing the motions and iterating in simulation.

Dancing required a lot of strength and speed, so we even upgraded some of Atlas’ hardware to give it more power. Dance might be the highest power thing we’ve done to date—even though you might think parkour looks way more explosive, the amount of motion and speed that you have in dance is incredible. That also took a lot of time over the course of months; creating the capability in the machine to go along with the capability in the algorithms.

Once we had the final sequence that you see in the video, we only filmed for two days. Much of that time was spent figuring out how to move the camera through a scene with a bunch of robots in it to capture one continuous two-minute shot, and while we ran and filmed the dance routine multiple times, we could repeat it quite reliably. There was no cutting or splicing in that opening two-minute shot.

There were definitely some failures in the hardware that required maintenance, and our robots stumbled and fell down sometimes. These behaviors are not meant to be productized and to be a 100 percent reliable, but they’re definitely repeatable. We try to be honest with showing things that we can do, not a snippet of something that we did once. I think there’s an honesty required in saying that you’ve achieved something, and that’s definitely important for us.

You mentioned that Spot is now robust enough to dance all day. How about Atlas? If you kept on replacing its batteries, could it dance all day, too?

Atlas, as a machine, is still, you know… there are only a handful of them in the world, they’re complicated, and reliability was not a main focus. We would definitely break the robot from time to time. But the robustness of the hardware, in the context of what we were trying to do, was really great. And without that robustness, we wouldn’t have been able to make the video at all. I think Atlas is a little more like a helicopter, where there’s a higher ratio between the time you spend doing maintenance and the time you spend operating. Whereas with Spot, the expectation is that it’s more like a car, where you can run it for a long time before you have to touch it.

When you’re teaching Atlas to do new things, is it using any kind of machine learning? And if not, why not?

As a company, we’ve explored a lot of things, but Atlas is not using a learning controller right now. I expect that a day will come when we will. Atlas’ current dance performance uses a mixture of what we like to call reflexive control, which is a combination of reacting to forces, online and offline trajectory optimization, and model predictive control. We leverage these techniques because they’re a reliable way of unlocking really high performance stuff, and we understand how to wield these tools really well. We haven’t found the end of the road in terms of what we can do with them.

We plan on using learning to extend and build on the foundation of software and hardware that we’ve developed, but I think that we, along with the community, are still trying to figure out where the right places to apply these tools are. I think you’ll see that as part of our natural progression.

Image: Boston Dynamics

Much of Atlas’ dynamic motion comes from its lower body at the moment, but parkour makes use of upper body strength and agility as well, and we’ve seen some recent concept images showing Atlas doing vaults and pullups. Can you tell us more?

Humans and animals do amazing things using their legs, but they do even more amazing things when they use their whole bodies. I think parkour provides a fantastic framework that allows us to progress towards whole body mobility. Walking and running was just the start of that journey. We’re progressing through more complex dynamic behaviors like jumping and spinning, that’s what we’ve been working on for the last couple of years. And the next step is to explore how using arms to push and pull on the world could extend that agility.

One of the missions that I’ve given to the Atlas team is to start working on leveraging the arms as much as we leverage the legs to enhance and extend our mobility, and I’m really excited about what we’re going to be working on over the next couple of years, because it’s going to open up a lot more opportunities for us to do exciting stuff with Atlas.

What’s your perspective on hydraulic versus electric actuators for highly dynamic robots?

Across my career at Boston Dynamics, I’ve felt passionately connected to so many different types of technology, but I’ve settled into a place where I really don’t think this is an either-or conversation anymore. I think the selection of actuator technology really depends on the size of the robot that you’re building, what you want that robot to do, where you want it to go, and many other factors. Ultimately, it’s good to have both kinds of actuators in your toolbox, and I love having access to both—and we’ve used both with great success to make really impressive dynamic machines.

I think the only delineation between hydraulic and electric actuators that appears to be distinct for me is probably in scale. It’s really challenging to make tiny hydraulic things because the industry just doesn’t do a lot of that, and the reciprocal is that the industry also doesn’t tend to make massive electrical things. So, you may find that to be a natural division between these two technologies.

Besides what you’re working on at Boston Dynamics, what recent robotics research are you most excited about?

For us as a company, we really love to follow advances in sensing, computer vision, terrain perception, these are all things where the better they get, the more we can do. For me personally, one of the things I like to follow is manipulation research, and in particular manipulation research that advances our understanding of complex, friction-based interactions like sliding and pushing, or moving compliant things like ropes.

We’re seeing a shift from just pinching things, lifting them, moving them, and dropping them, to much more meaningful interactions with the environment. Research in that type of manipulation I think is going to unlock the potential for mobile manipulators, and I think it’s really going to open up the ability for robots to interact with the world in a rich way.

Is there anything else you’d like people to take away from this video?

For me personally, and I think it’s because I spend so much of my time immersed in robotics and have a deep appreciation for what a robot is and what its capabilities and limitations are, one of my strong desires is for more people to spend more time with robots. We see a lot of opinions and ideas from people looking at our videos on YouTube, and it seems to me that if more people had opportunities to think about and learn about and spend time with robots, that new level of understanding could help them imagine new ways in which robots could be useful in our daily lives. I think the possibilities are really exciting, and I just want more people to be able to take that journey. Continue reading →

Posted in Human Robots

#437905 New Deep Learning Method Helps Robots ...

Posted on December 14, 2020 by Android

One of the biggest things standing in the way of the robot revolution is their inability to adapt. That may be about to change though, thanks to a new approach that blends pre-learned skills on the fly to tackle new challenges.

Put a robot in a tightly-controlled environment and it can quickly surpass human performance at complex tasks, from building cars to playing table tennis. But throw these machines a curve ball and they’re in trouble—just check out this compilation of some of the world’s most advanced robots coming unstuck in the face of notoriously challenging obstacles like sand, steps, and doorways.

The reason robots tend to be so fragile is that the algorithms that control them are often manually designed. If they encounter a situation the designer didn’t think of, which is almost inevitable in the chaotic real world, then they simply don’t have the tools to react.

Rapid advances in AI have provided a potential workaround by letting robots learn how to carry out tasks instead of relying on hand-coded instructions. A particularly promising approach is deep reinforcement learning, where the robot interacts with its environment through a process of trial-and-error and is rewarded for carrying out the correct actions. Over many repetitions it can use this feedback to learn how to accomplish the task at hand.

But the approach requires huge amounts of data to solve even simple tasks. And most of the things we would want a robot to do are actually comprised of many smaller tasks—for instance, delivering a parcel involves learning how to pick an object up, how to walk, how to navigate, and how to pass an object to someone else, among other things.

Training all these sub-tasks simultaneously is hugely complex and far beyond the capabilities of most current AI systems, so many experiments so far have focused on narrow skills. Some have tried to train AI on multiple skills separately and then use an overarching system to flip between these expert sub-systems, but these approaches still can’t adapt to completely new challenges.

Building off this research, though, scientists have now created a new AI system that can blend together expert sub-systems specialized for a specific task. In a paper in Science Robotics, they explain how this allows a four-legged robot to improvise new skills and adapt to unfamiliar challenges in real time.

The technique, dubbed multi-expert learning architecture (MELA), relies on a two-stage training approach. First the researchers used a computer simulation to train two neural networks to carry out two separate tasks: trotting and recovering from a fall.

They then used the models these two networks learned as seeds for eight other neural networks specialized for more specific motor skills, like rolling over or turning left or right. The eight “expert networks” were trained simultaneously along with a “gating network,” which learns how to combine these experts to solve challenges.

Because the gating network synthesizes the expert networks rather than switching them on sequentially, MELA is able to come up with blends of different experts that allow it to tackle problems none could solve alone.

The authors liken the approach to training people in how to play soccer. You start out by getting them to do drills on individual skills like dribbling, passing, or shooting. Once they’ve mastered those, they can then intelligently combine them to deal with more dynamic situations in a real game.

After training the algorithm in simulation, the researchers uploaded it to a four-legged robot and subjected it to a battery of tests, both indoors and outdoors. The robot was able to adapt quickly to tricky surfaces like gravel or pebbles, and could quickly recover from being repeatedly pushed over before continuing on its way.

There’s still some way to go before the approach could be adapted for real-world commercially useful robots. For a start, MELA currently isn’t able to integrate visual perception or a sense of touch; it simply relies on feedback from the robot’s joints to tell it what’s going on around it. The more tasks you ask the robot to master, the more complex and time-consuming the training will get.

Nonetheless, the new approach points towards a promising way to make multi-skilled robots become more than the sum of their parts. As much fun as it is, it seems like laughing at compilations of clumsy robots may soon be a thing of the past.

Image Credit: Yang et al., Science Robotics Continue reading →

Posted in Human Robots

#437901 How computer simulation will accelerate ...

Posted on December 14, 2020 by Android

Jeffrey C. Trinkle has always had a keen interest in robot hands. And, though it may be a long way off, Trinkle, who has studied robotics for more than thirty years, says he's most compelled by the prospect of robots performing “dexterous manipulation” at the level of a human “or beyond.” Continue reading →

Posted in Human Robots

#437882 Video Friday: MIT Mini-Cheetah Robots ...

Posted on December 11, 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!):

ICCR 2020 – December 26-29, 2020 – [Online Conference]
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.

What a lovely Christmas video from Norlab.

[ Norlab ]

Thanks Francois!

MIT Mini-Cheetahs are looking for a new home. Our new cheetah cubs, born at NAVER LABS, are for the MIT Mini-Cheetah workshop. MIT professor Sangbae Kim and his research team are supporting joint research by distributing Mini-Cheetahs to researchers all around the world.

[ NAVER Labs ]

For several years, NVIDIA’s research teams have been working to leverage GPU technology to accelerate reinforcement learning (RL). As a result of this promising research, NVIDIA is pleased to announce a preview release of Isaac Gym – NVIDIA’s physics simulation environment for reinforcement learning research. RL-based training is now more accessible as tasks that once required thousands of CPU cores can now instead be trained using a single GPU.

[ NVIDIA ]

At SINTEF in Norway, they're working on ways of using robots to keep tabs on giant floating cages of tasty fish:

One of the tricky things about operating robots in an environment like this is localization, so SINTEF is working on a solution that uses beacons:

While that video shows a lot of simulation (because otherwise there are tons of fish in the way), we're told that the autonomous navigation has been successfully demonstrated with an ROV in “a full scale fish farm with up to 200.000 salmon swimming around the robot.”

[ SINTEF ]

Thanks Eleni!

We’ve been getting ready for the snow in the most BG way possible. Wishing all of you a happy and healthy holiday season.

[ Berkshire Grey ]

ANYbotics doesn’t care what time of the year it is, so Happy Easter!

And here's a little bit about why ANYmal C looks the way it does.

[ ANYbotics ]

Robert “Buz” Chmielewski is using two modular prosthetic limbs developed by APL to feed himself dessert. Smart software puts his utensils in roughly the right spot, and then Buz uses his brain signals to cut the food with knife and fork. Once he is done cutting, the software then brings the food near his mouth, where he again uses brain signals to bring the food the last several inches to his mouth so that he can eat it.

[ JHUAPL ]

Introducing VESPER: a new military-grade small drone that is designed, sourced and built in the United States. Vesper offers a 50-minutes flight time, with speeds up to 45 mph (72 kph) and a total flight range of 25 miles (45 km). The magnetic snap-together architecture enables extremely fast transitions: the battery, props and rotor set can each be swapped in <5 seconds.

[ Vantage Robotics ]

In this video, a multi-material robot simulator is used to design a shape-changing robot, which is then transferred to physical hardware. The simulated and real robots can use shape change to switch between rolling gaits and inchworm gaits, to locomote in multiple environments.

[ Yale Faboratory ]

Get a preview of the cave environments that are being used to inspire the Final Event competition course of the DARPA Subterranean Challenge. In the Final Event, teams will deploy their robots to rapidly map, navigate, and search in competition courses that combine elements of man-made tunnel systems, urban underground, and natural cave networks!

The reason to pay attention this particular video is that it gives us some idea of what DARPA means when they say "cave."

[ SubT ]

MQ25 takes another step toward unmanned aerial refueling for the U.S. Navy. The MQ-25 test asset has flown for the first time with an aerial refueling pod containing the hose and basket that will make it an aerial refueler.

[ Boeing ]

We present a unified model-based and data-driven approach for quadrupedal planning and control to achieve dynamic locomotion over uneven terrain. We utilize on-board proprioceptive and exteroceptive feedback to map sensory information and desired base velocity commands into footstep plans using a reinforcement learning (RL) policy trained in simulation over a wide range of procedurally generated terrains.

[ DRS ]

The video shows the results of the German research project RoPHa. Within the project, the partners developed technologies for two application scenarios with the service robot Care-O-bot 4 in order to support people in need of help when eating.

[ RoPHa Project ]

Thanks Jenny!

This looks like it would be fun, if you are a crazy person.

[ Team BlackSheep ]

Robot accuracy is the limiting factor in many industrial applications. Manufacturers often only specify the pose repeatability values of their robotic systems. Fraunhofer IPA has set up a testing environment for automated measuring of accuracy performance criteria of industrial robots. Following the procedures defined in norm ISO 9283 allows generating reliable and repeatable results. They can be the basis for targeted measures increasing the robotic system’s accuracy.

[ Fraunhofer ]

Thanks Jenny!

The IEEE Women in Engineering – Robotics and Automation Society (WIE-RAS) hosted an online panel on best practices for teaching robotics. The diverse panel boasts experts in robotics education from a variety of disciplines, institutions, and areas of expertise.

[ IEEE RAS ]

Northwestern researchers have developed a first-of-its-kind soft, aquatic robot that is powered by light and rotating magnetic fields. These life-like robotic materials could someday be used as "smart" microscopic systems for production of fuels and drugs, environmental cleanup or transformative medical procedures.

[ Northwestern ]

Tech United Eindhoven's soccer robots now have eight wheels instead of four wheels, making them tweleve times better, if my math is right.

[ TU Eindhoven ] Continue reading →

Posted in Human Robots

#437864 Video Friday: Jet-Powered Flying ...

Posted on December 2, 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!):

ICRA 2020 – June 1-15, 2020 – [Virtual Conference]
RSS 2020 – July 12-16, 2020 – [Virtual Conference]
CLAWAR 2020 – August 24-26, 2020 – [Virtual Conference]
ICUAS 2020 – September 1-4, 2020 – Athens, Greece
ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
ICSR 2020 – November 14-16, 2020 – Golden, Colorado
Let us know if you have suggestions for next week, and enjoy today’s videos.

ICRA 2020, the world’s best, biggest, longest virtual robotics conference ever, kicked off last Sunday with an all-star panel on a critical topic: “COVID-19: How Can Roboticists Help?”

Watch other ICRA keynotes on IEEE.tv.

We’re getting closer! Well, kinda. iRonCub, the jet-powered flying humanoid, is still a simulation for now, but not only are the simulations getting better—the researchers have begun testing real jet engines!

This video shows the latest results on Aerial Humanoid Robotics obtained by the Dynamic Interaction Control Lab at the Italian Institute of Technology. The video simulates robot and jet dynamics, where the latter uses the results obtained in the paper “Modeling, Identification and Control of Model Jet Engines for Jet Powered Robotics” published in IEEE Robotics and Automation Letters.

This video presents the paper entitled “Modeling, Identification and Control of Model Jet Engines for Jet Powered Robotics” published in IEEE Robotics and Automation Letters (Volume: 5 , Issue: 2 , April 2020 ) Page(s): 2070 – 2077. Preprint at https://arxiv.org/pdf/1909.13296.pdf.​

[ IIT ]

In a new pair of papers, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with new tools to let robots better perceive what they’re interacting with: the ability to see and classify items, and a softer, delicate touch.

[ MIT CSAIL ]

UBTECH’s anti-epidemic solutions greatly relieve the workload of front-line medical staff and cut the consumption of personal protective equipment (PPE).

[ UBTECH ]

We demonstrate a method to assess the concrete deterioration in sewers by performing a tactile inspection motion with a sensorized foot of a legged robot.

[ THING ] via [ ANYmal Research ]

Get a closer look at the Virtual competition of the Urban Circuit and how teams can use the simulated environments to better prepare for the physical courses of the Subterranean Challenge.

[ SubT ]

Roboticists at the University of California San Diego have developed flexible feet that can help robots walk up to 40 percent faster on uneven terrain, such as pebbles and wood chips. The work has applications for search-and-rescue missions as well as space exploration.

[ UCSD ]

Thanks Ioana!

Tsuki is a ROS-enabled, highly dynamic quadruped robot developed by Lingkang Zhang.

And as far as we know, Lingkang is still chasing it.

[ Quadruped Tsuki ]

Thanks Lingkang!

Watch this.

This video shows an impressive demo of how YuMi’s superior precision, using precise servo gripper fingers and vacuum suction tool to pick up extremely small parts inside a mechanical watch. The video is not a final application used in production, it is a demo of how such an application can be implemented.

[ ABB ]

Meet Presso, the “5-minute dry cleaning robot.” Can you really call this a robot? We’re not sure. The company says it uses “soft robotics to hold the garment correctly, then clean, sanitize, press and dry under 5 minutes.” The machine was initially designed for use in the hospitality industry, but after adding a disinfectant function for COVID-19, it is now being used on movie and TV sets.

[ Presso ]

The next Mars rover launches next month (!), and here’s a look at some of the instruments on board.

[ JPL ]

Embodied Lead Engineer, Peter Teel, describes why we chose to build Moxie’s computing system from scratch and what makes it so unique.

[ Embodied ]

I did not know that this is where Pepper’s e-stop is. Nice design!

[ Softbank Robotics ]

State of the art in the field of swarm robotics lacks systems capable of absolute decentralization and is hence unable to mimic complex biological swarm systems consisting of simple units. Our research interconnects fields of swarm robotics and computer vision, and introduces novel use of a vision-based method UVDAR for mutual localization in swarm systems, allowing for absolute decentralization found among biological swarm systems. The developed methodology allows us to deploy real-world aerial swarming systems with robots directly localizing each other instead of communicating their states via a communication network, which is a typical bottleneck of current state of the art systems.

[ CVUT ]

I’m almost positive I could not do this task.

It’s easy to pick up objects using YuMi’s integrated vacuum functionality, it also supports ABB Robot’s Conveyor Tracking and Pickmaster 3 functionality, enabling it to track a moving conveyor and pick up objects using vision. Perfect for consumer products handling applications.

[ ABB ]

Cycling safety gestures, such as hand signals and shoulder checks, are an essential part of safe manoeuvring on the road. Child cyclists, in particular, might have difficulties performing safety gestures on the road or even forget about them, given the lack of cycling experience, road distractions and differences in motor and perceptual-motor abilities compared with adults. To support them, we designed two methods to remind about safety gestures while cycling. The first method employs an icon-based reminder in heads-up display (HUD) glasses and the second combines vibration on the handlebar and ambient light in the helmet. We investigated the performance of both methods in a controlled test-track experiment with 18 children using a mid-size tricycle, augmented with a set of sensors to recognize children’s behavior in real time. We found that both systems are successful in reminding children about safety gestures and have their unique advantages and disadvantages.

[ Paper ]

Nathan Sam and Robert “Red” Jensen fabricate and fly a Prandtl-M aircraft at NASA’s Armstrong Flight Research Center in California. The aircraft is the second of three prototypes of varying sizes to provide scientists with options to fly sensors in the Martian atmosphere to collect weather and landing site information for future human exploration of Mars.

[ NASA ]

This is clever: In order to minimize time spent labeling datasets, you can use radar to identify other vehicles, not because the radar can actually recognize other vehicles, but because the radar can recognize other stuff that’s big and moving, which turns out to be almost as good.

[ ICRA Paper ]

Happy 10th birthday to the Natural Robotics Lab at the University of Sheffield.

[ NRL ] Continue reading →

Posted in Human Robots