Tag Archives: robotics
#437935 Start the New Year Right: By Watching ...
I don’t need to tell you that 2020 was a tough year. There was almost nothing good about it, and we saw it off with a “good riddance” and hopes for a better 2021. But robotics company Boston Dynamics took a different approach to closing out the year: when all else fails, why not dance?
The company released a video last week that I dare you to watch without laughing—or at the very least, cracking a pretty big smile. Because, well, dancing robots are funny. And it’s not just one dancing robot, it’s four of them: two humanoid Atlas bots, one four-legged Spot, and one Handle, a bot-on-wheels built for materials handling.
The robots’ killer moves look almost too smooth and coordinated to be real, leading many to speculate that the video was computer-generated. But if you can trust Elon Musk, there’s no CGI here.
This is not CGI https://t.co/VOivE97vPR
— Elon Musk (@elonmusk) December 29, 2020
Boston Dynamics went through a lot of changes in the last ten years; it was acquired by Google in 2013, then sold to Japanese conglomerate SoftBank in 2017 before being acquired again by Hyundai just a few weeks ago for $1.1 billion. But this isn’t the first time they teach a robot to dance and make a video for all the world to enjoy; Spot tore up the floor to “Uptown Funk” back in 2018.
Four-legged Spot went commercial in June, with a hefty price tag of $74,500, and was put to some innovative pandemic-related uses, including remotely measuring patients’ vital signs and reminding people to social distance.
Hyundai plans to implement its newly-acquired robotics prowess for everything from service and logistics robots to autonomous driving and smart factories.
They’ll have their work cut out for them. Besides being hilarious, kind of heartwarming, and kind of creepy all at once, the robots’ new routine is pretty impressive from an engineering standpoint. Compare it to a 2016 video of Atlas trying to pick up a box (I know it’s a machine with no feelings, but it’s hard not to feel a little bit bad for it, isn’t it?), and it’s clear Boston Dynamics’ technology has made huge strides. It wouldn’t be surprising if, in two years’ time, we see a video of a flash mob of robots whose routine includes partner dancing and back flips (which, admittedly, Atlas can already do).
In the meantime, though, this one is pretty entertaining—and not a bad note on which to start the new year.
Image Credit: Boston Dynamics Continue reading
Posted in Human Robots
#437924 How a Software Map of the Entire Planet ...
i
“3D map data is the scaffolding of the 21st century.”
–Edward Miller, Founder, Scape Technologies, UK
Covered in cameras, sensors, and a distinctly spaceship looking laser system, Google’s autonomous vehicles were easy to spot when they first hit public roads in 2015. The key hardware ingredient is a spinning laser fixed to the roof, called lidar, which provides the car with a pair of eyes to see the world. Lidar works by sending out beams of light and measuring the time it takes to bounce off objects back to the source. By timing the light’s journey, these depth-sensing systems construct fully 3D maps of their surroundings.
3D maps like these are essentially software copies of the real world. They will be crucial to the development of a wide range of emerging technologies including autonomous driving, drone delivery, robotics, and a fast-approaching future filled with augmented reality.
Like other rapidly improving technologies, lidar is moving quickly through its development cycle. What was an expensive technology on the roof of a well-funded research project is now becoming cheaper, more capable, and readily available to consumers. At some point, lidar will come standard on most mobile devices and is now available to early-adopting owners of the iPhone 12 Pro.
Consumer lidar represents the inevitable shift from wealthy tech companies generating our world’s map data, to a more scalable crowd-sourced approach. To develop the repository for their Street View Maps product, Google reportedly spent $1-2 billion sending cars across continents photographing every street. Compare that to a live-mapping service like Waze, which uses crowd-sourced user data from its millions of users to generate accurate and real-time traffic conditions. Though these maps serve different functions, one is a static, expensive, unchanging map of the world while the other is dynamic, real-time, and constructed by users themselves.
Soon millions of people may be scanning everything from bedrooms to neighborhoods, resulting in 3D maps of significant quality. An online search for lidar room scans demonstrates just how richly textured these three-dimensional maps are compared to anything we’ve had before. With lidar and other depth-sensing systems, we now have the tools to create exact software copies of everywhere and everything on earth.
At some point, likely aided by crowdsourcing initiatives, these maps will become living breathing, real-time representations of the world. Some refer to this idea as a “digital twin” of the planet. In a feature cover story, Kevin Kelly, the cofounder of Wired magazine, calls this concept the “mirrorworld,” a one-to-one software map of everything.
So why is that such a big deal? Take augmented reality as an example.
Of all the emerging industries dependent on such a map, none are more invested in seeing this concept emerge than those within the AR landscape. Apple, for example, is not-so-secretly developing a pair of AR glasses, which they hope will deliver a mainstream turning point for the technology.
For Apple’s AR devices to work as anticipated, they will require virtual maps of the world, a concept AR insiders call the “AR cloud,” which is synonymous with the “mirrorworld” concept. These maps will be two things. First, they will be a tool that creators use to place AR content in very specific locations; like a world canvas to paint on. Second, they will help AR devices both locate and understand the world around them so they can render content in a believable way.
Imagine walking down a street wanting to check the trading hours of a local business. Instead of pulling out your phone to do a tedious search online, you conduct the equivalent of a visual google search simply by gazing at the store. Albeit a trivial example, the AR cloud represents an entirely non-trivial new way of managing how we organize the world’s information. Access to knowledge can be shifted away from the faraway monitors in our pocket, to its relevant real-world location.
Ultimately this describes a blurring of physical and digital infrastructure. Our public and private spaces will thus be comprised equally of both.
No example demonstrates this idea better than Pokémon Go. The game is straightforward enough; users capture virtual characters scattered around the real world. Today, the game relies on traditional GPS technology to place its characters, but GPS is accurate only to within a few meters of a location. For a car navigating on a highway or locating Pikachus in the world, that level of precision is sufficient. For drone deliveries, driverless cars, or placing a Pikachu in a specific location, say on a tree branch in a park, GPS isn’t accurate enough. As astonishing as it may seem, many experimental AR cloud concepts, even entirely mapped cities, are location specific down to the centimeter.
Niantic, the $4 billion publisher behind Pokémon Go, is aggressively working on developing a crowd-sourced approach to building better AR Cloud maps by encouraging their users to scan the world for them. Their recent acquisition of 6D.ai, a mapping software company developed by the University of Oxford’s Victor Prisacariu through his work at Oxford’s Active Vision Lab, indicates Niantic’s ambition to compete with the tech giants in this space.
With 6D.ai’s technology, Niantic is developing the in-house ability to generate their own 3D maps while gaining better semantic understanding of the world. By going beyond just knowing there’s a temporary collection of orange cones in a certain location, for example, the game may one day understand the meaning behind this; that a temporary construction zone means no Pokémon should spawn here to avoid drawing players to this location.
Niantic is not the only company working on this. Many of the big tech firms you would expect have entire teams focused on map data. Facebook, for example, recently acquired the UK-based Scape technologies, a computer vision startup mapping entire cities with centimeter precision.
As our digital maps of the world improve, expect a relentless and justified discussion of privacy concerns as well. How will society react to the idea of a real-time 3D map of their bedroom living on a Facebook or Amazon server? Those horrified by the use of facial recognition AI being used in public spaces are unlikely to find comfort in the idea of a machine-readable world subject to infinite monitoring.
The ability to build high-precision maps of the world could reshape the way we engage with our planet and promises to be one of the biggest technology developments of the next decade. While these maps may stay hidden as behind-the-scenes infrastructure powering much flashier technologies that capture the world’s attention, they will soon prop up large portions of our technological future.
Keep that in mind when a car with no driver is sharing your road.
Image credit: sergio souza / Pexels Continue reading
Posted in Human Robots
#437918 Video Friday: These Robots Wish You ...
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]
HRI 2021 – March 8-11, 2021 – [Online]
RoboSoft 2021 – April 12-16, 2021 – [Online]
Let us know if you have suggestions for next week, and enjoy today's videos.
Look who’s baaaack: Jibo! After being sold (twice?), this pioneering social home robot (it was first announced back in 2014!) now belongs to NTT Disruption, which was described to us as the “disruptive company of NTT Group.” We are all for disruption, so this looks like a great new home for Jibo.
[ NTT Disruption ]
Thanks Ana!
FZI's Christmas Party was a bit of a challenge this year; good thing robots are totally competent to have a part on their own.
[ FZI ]
Thanks Arne!
Do you have a lonely dog that just wants a friend to watch cat videos on YouTube with? The Danish Technological Institute has a gift idea for you.
[ DTI ]
Thanks Samuel!
Once upon a time, not so far away, there was an elf who received a very special gift. Watch this heartwarming story. Happy Holidays from the Robotiq family to yours!
Of course, these elves are not now unemployed, they've instead moved over to toy design full time!
[ Robotiq ]
An elegant Christmas video from the Dynamics System Lab, make sure and watch through the very end for a little extra cheer.
[ Dynamic Systems Lab ]
Thanks Angela!
Usually I complain when robotics companies make holiday videos without any real robots in them, but this is pretty darn cute from Yaskawa this year.
[ Yaskawa ]
Here's our little christmas gift to the fans of strange dynamic behavior. The gyro will follow any given shape as soon as the tip touches its edge and the rotation is fast enough. The friction between tip and shape generates a tangential force, creating a moment such that the gyroscopic reaction pushes the tip towards the shape. The resulting normal force produces a moment that guides the tip along the shape's edge.
[ TUM ]
Happy Holidays from Fanuc!
Okay but why does there have to be an assembly line elf just to put in those little cranks?
[ Fanuc ]
Astrobotic's cute little CubeRover is at NASA busy not getting stuck in places.
[ Astrobotic ]
Team CoSTAR is sharing more of their work on subterranean robotic exploration.
[ CoSTAR ]
Skydio Autonomy Enterprise Foundation (AEF), a new software product that delivers advanced AI-powered capabilities to assist the pilot during tactical situational awareness scenarios and detailed industrial asset inspections. Designed for professionals, it offers an enterprise-caliber flight experience through the new Skydio Enterprise application.
[ Skydio ]
GITAI's S1 autonomous robot will conduct two experiments: IVA (Intra-Vehicular Activity) tasks such as switch and cable operations, and assembly of structures and panels to demonstrate its capability for ISA (In-Space Assembly) tasks. This video was recorded in the Nanoracks Bishop Airlock mock-up facility @GITAI Tokyo office.
[ GITAI ]
It's no Atlas, but this is some impressive dynamic balancing from iCub.
[ IIT ]
The Campaign to Stop Killer Robots and I don't agree on a lot of things, and I don't agree with a lot of the assumptions made in this video, either. But, here you go!
[ CSKR ]
I don't know much about this robot, but I love it.
[ Columbia ]
Most cable-suspended robots have a very well defined workspace, but you can increase that workspace by swinging them around. Wheee!
[ Laval ]
How you know your robot's got some skill: “to evaluate the performance in climbing over the step, we compared the R.L. result to the results of 12 students who attempted to find the best planning. The RL outperformed all the group, in terms of effort and time, both in continuous (joystick) and partition planning.”
[ Zarrouk Lab ]
In the Spring 2021 semester, mechanical engineering students taking MIT class 2.007, Design and Manufacturing I, will be able to participate in the class’ iconic final robot competition from the comfort of their own home. Whether they take the class virtually or semi-virtually, students will be sent a massive kit of tools and materials to build their own unique robot along with a “Home Alone” inspired game board for the final global competition.
[ MIT ]
Well, this thing is still around!
[ Moley Robotics ]
Manuel Ahumada wrote in to share this robotic Baby Yoda that he put together with a little bit of help from Intel's OpenBot software.
[ YouTube ]
Thanks Manuel!
Here's what Zoox has been working on for the past half-decade.
[ Zoox ] Continue reading
Posted in Human Robots
#437905 New Deep Learning Method Helps Robots ...
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