Tag Archives: rapid
#435640 Video Friday: This Wearable Robotic Tail ...
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!):
DARPA SubT Tunnel Circuit – August 15-22, 2019 – Pittsburgh, Pa., USA
CLAWAR 2019 – August 26-28, 2019 – Kuala Lumpur, Malaysia
IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.
Lakshmi Nair from Georgia Tech describes some fascinating research towards robots that can create their own tools, as presented at ICRA this year:
Using a novel capability to reason about shape, function, and attachment of unrelated parts, researchers have for the first time successfully trained an intelligent agent to create basic tools by combining objects.
The breakthrough comes from Georgia Tech’s Robot Autonomy and Interactive Learning (RAIL) research lab and is a significant step toward enabling intelligent agents to devise more advanced tools that could prove useful in hazardous – and potentially life-threatening – environments.
[ Lakshmi Nair ]
Victor Barasuol, from the Dynamic Legged Systems Lab at IIT, wrote in to share some new research on their HyQ quadruped that enables sensorless shin collision detection. This helps the robot navigate unstructured environments, and also mitigates all those painful shin strikes, because ouch.
This will be presented later this month at the International Conference on Climbing and Walking Robots (CLAWAR) in Kuala Lumpur, Malaysia.
[ IIT ]
Thanks Victor!
You used to have a tail, you know—as an embryo, about a month in to your development. All mammals used to have tails, and now we just have useless tailbones, which don’t help us with balancing even a little bit. BRING BACK THE TAIL!
The tail, created by Junichi Nabeshima, Kouta Minamizawa, and MHD Yamen Saraiji from Keio University’s Graduate School of Media Design, was presented at SIGGRAPH 2019 Emerging Technologies.
[ Paper ] via [ Gizmodo ]
The noises in this video are fantastic.
[ ESA ]
Apparently the industrial revolution wasn’t a thorough enough beatdown of human knitting, because the robots are at it again.
[ MIT CSAIL ]
Skydio’s drones just keep getting more and more impressive. Now if only they’d make one that I can afford…
[ Skydio ]
The only thing more fun than watching robots is watching people react to robots.
[ SEER ]
There aren’t any robots in this video, but it’s robotics-related research, and very soothing to watch.
[ Stanford ]
#autonomousicecreamtricycle
In case it wasn’t clear, which it wasn’t, this is a Roboy project. And if you didn’t understand that first video, you definitely won’t understand this second one:
Whatever that t-shirt is at the end (Roboy in sunglasses puking rainbows…?) I need one.
[ Roboy ]
By adding electronics and computation technology to a simple cane that has been around since ancient times, a team of researchers at Columbia Engineering have transformed it into a 21st century robotic device that can provide light-touch assistance in walking to the aged and others with impaired mobility.
The light-touch robotic cane, called CANINE, acts as a cane-like mobile assistant. The device improves the individual’s proprioception, or self-awareness in space, during walking, which in turn improves stability and balance.
[ ROAR Lab ]
During the second field experiment for DARPA’s OFFensive Swarm-Enabled Tactics (OFFSET) program, which took place at Fort Benning, Georgia, teams of autonomous air and ground robots tested tactics on a mission to isolate an urban objective. Similar to the way a firefighting crew establishes a boundary around a burning building, they first identified locations of interest and then created a perimeter around the focal point.
[ DARPA ]
I think there’s a bit of new footage here of Ghost Robotics’ Vision 60 quadruped walking around without sensors on unstructured terrain.
[ Ghost Robotics ]
If you’re as tired of passenger drone hype as I am, there’s absolutely no need to watch this video of NEC’s latest hover test.
[ AP ]
As researchers teach robots to perform more and more complex tasks, the need for realistic simulation environments is growing. Existing techniques for closing the reality gap by approximating real-world physics often require extensive real world data and/or thousands of simulation samples. This paper presents TuneNet, a new machine learning-based method to directly tune the parameters of one model to match another using an iterative residual tuning technique. TuneNet estimates the parameter difference between two models using a single observation from the target and minimal simulation, allowing rapid, accurate and sample-efficient parameter estimation.
The system can be trained via supervised learning over an auto-generated simulated dataset. We show that TuneNet can perform system identification, even when the true parameter values lie well outside the distribution seen during training, and demonstrate that simulators tuned with TuneNet outperform existing techniques for predicting rigid body motion. Finally, we show that our method can estimate real-world parameter values, allowing a robot to perform sim-to-real task transfer on a dynamic manipulation task unseen during training. We are also making a baseline implementation of our code available online.
[ Paper ]
Here’s an update on what GITAI has been up to with their telepresence astronaut-replacement robot.
[ GITAI ]
Curiosity captured this 360-degree panorama of a location on Mars called “Teal Ridge” on June 18, 2019. This location is part of a larger region the rover has been exploring called the “clay-bearing unit” on the side of Mount Sharp, which is inside Gale Crater. The scene is presented with a color adjustment that approximates white balancing to resemble how the rocks and sand would appear under daytime lighting conditions on Earth.
[ MSL ]
Some updates (in English) on ROS from ROSCon France. The first is a keynote from Brian Gerkey:
And this second video is from Omri Ben-Bassat, about how to keep your Anki Vector alive using ROS:
All of the ROSCon FR talks are available on Vimeo.
[ ROSCon FR ] Continue reading
Posted in Human Robots
#435619 Video Friday: Watch This Robot Dog ...
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!):
IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
RoboBusiness 2019 – October 1-3, 2019 – Santa Clara, CA, USA
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.
Team PLUTO (University of Pennsylvania, Ghost Robotics, and Exyn Technologies) put together this video giving us a robot’s-eye-view (or whatever they happen to be using for eyes) of the DARPA Subterranean Challenge tunnel circuits.
[ PLUTO ]
Zhifeng Huang has been improving his jet-stepping humanoid robot, which features new hardware and the ability to take larger and more complex steps.
This video reported the last progress of an ongoing project utilizing ducted-fan propulsion system to improve humanoid robot’s ability in stepping over large ditches. The landing point of the robot’s swing foot can be not only forward but also side direction. With keeping quasi-static balance, the robot was able to step over a ditch with 450mm in width (up to 97% of the robot’s leg’s length) in 3D stepping.
[ Paper ]
Thanks Zhifeng!
These underacuated hands from Matei Ciocarlie’s lab at Columbia are magically able to reconfigure themselves to grasp different object types with just one or two motors.
[ Paper ] via [ ROAM Lab ]
This is one reason we should pursue not “autonomous cars” but “fully autonomous cars” that never require humans to take over. We can’t be trusted.
During our early days as the Google self-driving car project, we invited some employees to test our vehicles on their commutes and weekend trips. What we were testing at the time was similar to the highway driver assist features that are now available on cars today, where the car takes over the boring parts of the driving, but if something outside its ability occurs, the driver has to take over immediately.
What we saw was that our testers put too much trust in that technology. They were doing things like texting, applying makeup, and even falling asleep that made it clear they would not be ready to take over driving if the vehicle asked them to. This is why we believe that nothing short of full autonomy will do.
[ Waymo ]
Buddy is a DIY and fetchingly minimalist social robot (of sorts) that will be coming to Kickstarter this month.
We have created a new arduino kit. His name is Buddy. He is a DIY social robot to serve as a replacement for Jibo, Cozmo, or any of the other bots that are no longer available. Fully 3D printed and supported he adds much more to our series of Arduino STEM robotics kits.
Buddy is able to look around and map his surroundings and react to changes within them. He can be surprised and he will always have a unique reaction to changes. The kit can be built very easily in less than an hour. It is even robust enough to take the abuse that kids can give it in a classroom.
[ Littlebots ]
The android Mindar, based on the Buddhist deity of mercy, preaches sermons at Kodaiji temple in Kyoto, and its human colleagues predict that with artificial intelligence it could one day acquire unlimited wisdom. Developed at a cost of almost $1 million (¥106 million) in a joint project between the Zen temple and robotics professor Hiroshi Ishiguro, the robot teaches about compassion and the dangers of desire, anger and ego.
[ Japan Times ]
I’m not sure whether it’s the sound or what, but this thing scares me for some reason.
[ BIRL ]
This gripper uses magnets as a sort of adjustable spring for dynamic stiffness control, which seems pretty clever.
[ Buffalo ]
What a package of medicine sees while being flown by drone from a hospital to a remote clinic in the Dominican Republic. The drone flew 11 km horizontally and 800 meters vertically, and I can’t even imagine what it would take to make that drive.
[ WeRobotics ]
My first ride in a fully autonomous car was at Stanford in 2009. I vividly remember getting in the back seat of a descendant of Junior, and watching the steering wheel turn by itself as the car executed a perfect parking maneuver. Ten years later, it’s still fun to watch other people have that experience.
[ Waymo ]
Flirtey, the pioneer of the commercial drone delivery industry, has unveiled the much-anticipated first video of its next-generation delivery drone, the Flirtey Eagle. The aircraft designer and manufacturer also unveiled the Flirtey Portal, a sophisticated take off and landing platform that enables scalable store-to-door operations; and an autonomous software platform that enables drones to deliver safely to homes.
[ Flirtey ]
EPFL scientists are developing new approaches for improved control of robotic hands – in particular for amputees – that combines individual finger control and automation for improved grasping and manipulation. This interdisciplinary proof-of-concept between neuroengineering and robotics was successfully tested on three amputees and seven healthy subjects.
[ EPFL ]
This video is a few years old, but we’ll take any excuse to watch the majestic sage-grouse be majestic in all their majesticness.
[ UC Davis ]
I like the idea of a game of soccer (or, football to you weirdos in the rest of the world) where the ball has a mind of its own.
[ Sphero ]
Looks like the whole delivery glider idea is really taking off! Or, you know, not taking off.
Weird that they didn’t show the landing, because it sure looked like it was going to plow into the side of the hill at full speed.
[ Yates ] via [ sUAS News ]
This video is from a 2018 paper, but it’s not like we ever get tired of seeing quadrupeds do stuff, right?
[ MIT ]
Founder and Head of Product, Ian Bernstein, and Head of Engineering, Morgan Bell, have been involved in the Misty project for years and they have learned a thing or two about building robots. Hear how and why Misty evolved into a robot development platform, learn what some of the earliest prototypes did (and why they didn’t work for what we envision), and take a deep dive into the technology decisions that form the Misty II platform.
[ Misty Robotics ]
Lex Fridman interviews Vijay Kumar on the Artifiical Intelligence Podcast.
[ AI Podcast ]
This week’s CMU RI Seminar is from Ross Knepper at Cornell, on Formalizing Teamwork in Human-Robot Interaction.
Robots out in the world today work for people but not with people. Before robots can work closely with ordinary people as part of a human-robot team in a home or office setting, robots need the ability to acquire a new mix of functional and social skills. Working with people requires a shared understanding of the task, capabilities, intentions, and background knowledge. For robots to act jointly as part of a team with people, they must engage in collaborative planning, which involves forming a consensus through an exchange of information about goals, capabilities, and partial plans. Often, much of this information is conveyed through implicit communication. In this talk, I formalize components of teamwork involving collaboration, communication, and representation. I illustrate how these concepts interact in the application of social navigation, which I argue is a first-class example of teamwork. In this setting, participants must avoid collision by legibly conveying intended passing sides via nonverbal cues like path shape. A topological representation using the braid groups enables the robot to reason about a small enumerable set of passing outcomes. I show how implicit communication of topological group plans achieves rapid covergence to a group consensus, and how a robot in the group can deliberately influence the ultimate outcome to maximize joint performance, yielding pedestrian comfort with the robot.
[ CMU RI ]
In this week’s episode of Robots in Depth, Per speaks with Julien Bourgeois about Claytronics, a project from Carnegie Mellon and Intel to develop “programmable matter.”
Julien started out as a computer scientist. He was always interested in robotics privately but then had the opportunity to get into micro robots when his lab was merged into the FEMTO-ST Institute. He later worked with Seth Copen Goldstein at Carnegie Mellon on the Claytronics project.
Julien shows an enlarged mock-up of the small robots that make up programmable matter, catoms, and speaks about how they are designed. Currently he is working on a unit that is one centimeter in diameter and he shows us the very small CPU that goes into that model.
[ Robots in Depth ] Continue reading
Posted in Human Robots
#435161 Less Like Us: An Alternate Theory of ...
The question of whether an artificial general intelligence will be developed in the future—and, if so, when it might arrive—is controversial. One (very uncertain) estimate suggests 2070 might be the earliest we could expect to see such technology.
Some futurists point to Moore’s Law and the increasing capacity of machine learning algorithms to suggest that a more general breakthrough is just around the corner. Others suggest that extrapolating exponential improvements in hardware is unwise, and that creating narrow algorithms that can beat humans at specialized tasks brings us no closer to a “general intelligence.”
But evolution has produced minds like the human mind at least once. Surely we could create artificial intelligence simply by copying nature, either by guided evolution of simple algorithms or wholesale emulation of the human brain.
Both of these ideas are far easier to conceive of than they are to achieve. The 302 neurons of the nematode worm’s brain are still an extremely difficult engineering challenge, let alone the 86 billion in a human brain.
Leaving aside these caveats, though, many people are worried about artificial general intelligence. Nick Bostrom’s influential book on superintelligence imagines it will be an agent—an intelligence with a specific goal. Once such an agent reaches a human level of intelligence, it will improve itself—increasingly rapidly as it gets smarter—in pursuit of whatever goal it has, and this “recursive self-improvement” will lead it to become superintelligent.
This “intelligence explosion” could catch humans off guard. If the initial goal is poorly specified or malicious, or if improper safety features are in place, or if the AI decides it would prefer to do something else instead, humans may be unable to control our own creation. Bostrom gives examples of how a seemingly innocuous goal, such as “Make everyone happy,” could be misinterpreted; perhaps the AI decides to drug humanity into a happy stupor, or convert most of the world into computing infrastructure to pursue its goal.
Drexler and Comprehensive AI Services
These are increasingly familiar concerns for an AI that behaves like an agent, seeking to achieve its goal. There are dissenters to this picture of how artificial general intelligence might arise. One notable alternative point of view comes from Eric Drexler, famous for his work on molecular nanotechnology and Engines of Creation, the book that popularized it.
With respect to AI, Drexler believes our view of an artificial intelligence as a single “agent” that acts to maximize a specific goal is too narrow, almost anthropomorphizing AI, or modeling it as a more realistic route towards general intelligence. Instead, he proposes “Comprehensive AI Services” (CAIS) as an alternative route to artificial general intelligence.
What does this mean? Drexler’s argument is that we should look more closely at how machine learning and AI algorithms are actually being developed in the real world. The optimization effort is going into producing algorithms that can provide services and perform tasks like translation, music recommendations, classification, medical diagnoses, and so forth.
AI-driven improvements in technology, argues Drexler, will lead to a proliferation of different algorithms: technology and software improvement, which can automate increasingly more complicated tasks. Recursive improvement in this regime is already occurring—take the newer versions of AlphaGo, which can learn to improve themselves by playing against previous versions.
Many Smart Arms, No Smart Brain
Instead of relying on some unforeseen breakthrough, the CAIS model of AI just assumes that specialized, narrow AI will continue to improve at performing each of its tasks, and the range of tasks that machine learning algorithms will be able to perform will become wider. Ultimately, once a sufficient number of tasks have been automated, the services that an AI will provide will be so comprehensive that they will resemble a general intelligence.
One could then imagine a “general” intelligence as simply an algorithm that is extremely good at matching the task you ask it to perform to the specialized service algorithm that can perform that task. Rather than acting like a single brain that strives to achieve a particular goal, the central AI would be more like a search engine, looking through the tasks it can perform to find the closest match and calling upon a series of subroutines to achieve the goal.
For Drexler, this is inherently a safety feature. Rather than Bostrom’s single, impenetrable, conscious and superintelligent brain (which we must try to psychoanalyze in advance without really knowing what it will look like), we have a network of capabilities. If you don’t want your system to perform certain tasks, you can simply cut it off from access to those services. There is no superintelligent consciousness to outwit or “trap”: more like an extremely high-level programming language that can respond to complicated commands by calling upon one of the myriad specialized algorithms that have been developed by different groups.
This skirts the complex problem of consciousness and all of the sticky moral quandaries that arise in making minds that might be like ours. After all, if you could simulate a human mind, you could simulate it experiencing unimaginable pain. Black Mirror-esque dystopias where emulated minds have no rights and are regularly “erased” or forced to labor in dull and repetitive tasks, hove into view.
Drexler argues that, in this world, there is no need to ever build a conscious algorithm. Yet it seems likely that, at some point, humans will attempt to simulate our own brains, if only in the vain attempt to pursue immortality. This model cannot hold forever. Yet its proponents argue that any world in which we could develop general AI would probably also have developed superintelligent capabilities in a huge range of different tasks, such as computer programming, natural language understanding, and so on. In other words, CAIS arrives first.
The Future In Our Hands?
Drexler argues that his model already incorporates many of the ideas from general AI development. In the marketplace, algorithms compete all the time to perform these services: they undergo the same evolutionary pressures that lead to “higher intelligence,” but the behavior that’s considered superior is chosen by humans, and the nature of the “general intelligence” is far more shaped by human decision-making and human programmers. Development in AI services could still be rapid and disruptive.
But in Drexler’s case, the research and development capacity comes from humans and organizations driven by the desire to improve algorithms that are performing individualized and useful tasks, rather than from a conscious AI recursively reprogramming and improving itself.
In other words, this vision does not absolve us of the responsibility of making our AI safe; if anything, it gives us a greater degree of responsibility. As more and more complex “services” are automated, performing what used to be human jobs at superhuman speed, the economic disruption will be severe.
Equally, as machine learning is trusted to carry out more complex decisions, avoiding algorithmic bias becomes crucial. Shaping each of these individual decision-makers—and trying to predict the complex ways they might interact with each other—is no less daunting a task than specifying the goal for a hypothetical, superintelligent, God-like AI. Arguably, the consequences of the “misalignment” of these services algorithms are already multiplying around us.
The CAIS model bridges the gap between real-world AI, machine learning developments, and real-world safety considerations, as well as the speculative world of superintelligent agents and the safety considerations involved with controlling their behavior. We should keep our minds open as to what form AI and machine learning will take, and how it will influence our societies—and we must take care to ensure that the systems we create don’t end up forcing us all to live in a world of unintended consequences.
Image Credit: MF Production/Shutterstock.com Continue reading
Posted in Human Robots