Tag Archives: training

#439251 Is AI the Future of Training for New ...

Everywhere you look in technology today, you find buzz about the promise of emergent technologies such as machine learning (ML) and artificial intelligence (AI). From curating the content that we watch on streaming services to finding ways to improve intense logistical processes, ML- and AI-based technologies already impact our lives in many ways. Increasingly, these …

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Posted in Human Robots

#439105 This Robot Taught Itself to Walk in a ...

Recently, in a Berkeley lab, a robot called Cassie taught itself to walk, a little like a toddler might. Through trial and error, it learned to move in a simulated world. Then its handlers sent it strolling through a minefield of real-world tests to see how it’d fare.

And, as it turns out, it fared pretty damn well. With no further fine-tuning, the robot—which is basically just a pair of legs—was able to walk in all directions, squat down while walking, right itself when pushed off balance, and adjust to different kinds of surfaces.

It’s the first time a machine learning approach known as reinforcement learning has been so successfully applied in two-legged robots.

This likely isn’t the first robot video you’ve seen, nor the most polished.

For years, the internet has been enthralled by videos of robots doing far more than walking and regaining their balance. All that is table stakes these days. Boston Dynamics, the heavyweight champ of robot videos, regularly releases mind-blowing footage of robots doing parkour, back flips, and complex dance routines. At times, it can seem the world of iRobot is just around the corner.

This sense of awe is well-earned. Boston Dynamics is one of the world’s top makers of advanced robots.

But they still have to meticulously hand program and choreograph the movements of the robots in their videos. This is a powerful approach, and the Boston Dynamics team has done incredible things with it.

In real-world situations, however, robots need to be robust and resilient. They need to regularly deal with the unexpected, and no amount of choreography will do. Which is how, it’s hoped, machine learning can help.

Reinforcement learning has been most famously exploited by Alphabet’s DeepMind to train algorithms that thrash humans at some the most difficult games. Simplistically, it’s modeled on the way we learn. Touch the stove, get burned, don’t touch the damn thing again; say please, get a jelly bean, politely ask for another.

In Cassie’s case, the Berkeley team used reinforcement learning to train an algorithm to walk in a simulation. It’s not the first AI to learn to walk in this manner. But going from simulation to the real world doesn’t always translate.

Subtle differences between the two can (literally) trip up a fledgling robot as it tries out its sim skills for the first time.

To overcome this challenge, the researchers used two simulations instead of one. The first simulation, an open source training environment called MuJoCo, was where the algorithm drew upon a large library of possible movements and, through trial and error, learned to apply them. The second simulation, called Matlab SimMechanics, served as a low-stakes testing ground that more precisely matched real-world conditions.

Once the algorithm was good enough, it graduated to Cassie.

And amazingly, it didn’t need further polishing. Said another way, when it was born into the physical world—it knew how to walk just fine. In addition, it was also quite robust. The researchers write that two motors in Cassie’s knee malfunctioned during the experiment, but the robot was able to adjust and keep on trucking.

Other labs have been hard at work applying machine learning to robotics.

Last year Google used reinforcement learning to train a (simpler) four-legged robot. And OpenAI has used it with robotic arms. Boston Dynamics, too, will likely explore ways to augment their robots with machine learning. New approaches—like this one aimed at training multi-skilled robots or this one offering continuous learning beyond training—may also move the dial. It’s early yet, however, and there’s no telling when machine learning will exceed more traditional methods.

And in the meantime, Boston Dynamics bots are testing the commercial waters.

Still, robotics researchers, who were not part of the Berkeley team, think the approach is promising. Edward Johns, head of Imperial College London’s Robot Learning Lab, told MIT Technology Review, “This is one of the most successful examples I have seen.”

The Berkeley team hopes to build on that success by trying out “more dynamic and agile behaviors.” So, might a self-taught parkour-Cassie be headed our way? We’ll see.

Image Credit: University of California Berkeley Hybrid Robotics via YouTube Continue reading

Posted in Human Robots

#439100 Video Friday: Robotic Eyeball Camera

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
RoboCup 2021 – June 22-28, 2021 – [Online Event]
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.

What if seeing devices looked like us? Eyecam is a prototype exploring the potential future design of sensing devices. Eyecam is a webcam shaped like a human eye that can see, blink, look around and observe us.

And it's open source, so you can build your own!

[ Eyecam ]

Looks like Festo will be turning some of its bionic robots into educational kits, which is a pretty cool idea.

[ Bionics4Education ]

Underwater soft robots are challenging to model and control because of their high degrees of freedom and their intricate coupling with water. In this paper, we present a method that leverages the recent development in differentiable simulation coupled with a differentiable, analytical hydrodynamic model to assist with the modeling and control of an underwater soft robot. We apply this method to Starfish, a customized soft robot design that is easy to fabricate and intuitive to manipulate.

[ MIT CSAIL ]

Rainbow Robotics, the company who made HUBO, has a new collaborative robot arm.

[ Rainbow Robotics ]

Thanks Fan!

We develop an integrated robotic platform for advanced collaborative robots and demonstrates an application of multiple robots collaboratively transporting an object to different positions in a factory environment. The proposed platform integrates a drone, a mobile manipulator robot, and a dual-arm robot to work autonomously, while also collaborating with a human worker. The platform also demonstrates the potential of a novel manufacturing process, which incorporates adaptive and collaborative intelligence to improve the efficiency of mass customization for the factory of the future.

[ Paper ]

Thanks Poramate!

In Sevastopol State University the team of the Laboratory of Underwater Robotics and Control Systems and Research and Production Association “Android Technika” performed tests of an underwater anropomorphic manipulator robot.

[ Sevastopol State ]

Thanks Fan!

Taiwanese company TCI Gene created a COVID test system based on their fully automated and enclosed gene testing machine QVS-96S. The system includes two ABB robots and carries out 1800 tests per day, operating 24/7. Every hour 96 virus samples tests are made with an accuracy of 99.99%.

[ ABB ]

A short video showing how a Halodi Robotics can be used in a commercial guarding application.

[ Halodi ]

During the past five years, under the NASA Early Space Innovations program, we have been developing new design optimization methods for underactuated robot hands, aiming to achieve versatile manipulation in highly constrained environments. We have prototyped hands for NASA’s Astrobee robot, an in-orbit assistive free flyer for the International Space Station.

[ ROAM Lab ]

The new, improved OTTO 1500 is a workhorse AMR designed to move heavy payloads through demanding environments faster than any other AMR on the market, with zero compromise to safety.

[ ROAM Lab ]

Very, very high performance sensing and actuation to pull this off.

[ Ishikawa Group ]

We introduce a conversational social robot designed for long-term in-home use to help with loneliness. We present a novel robot behavior design to have simple self-reflection conversations with people to improve wellness, while still being feasible, deployable, and safe.

[ HCI Lab ]

We are one of the 5 winners of the Start-up Challenge. This video illustrates what we achieved during the Swisscom 5G exploration week. Our proof-of-concept tele-excavation system is composed of a Menzi Muck M545 walking excavator automated & customized by Robotic Systems Lab and IBEX motion platform as the operator station. The operator and remote machine are connected for the first time via a 5G network infrastructure which was brought to our test field by Swisscom.

[ RSL ]

This video shows LOLA balancing on different terrain when being pushed in different directions. The robot is technically blind, not using any camera-based or prior information on the terrain (hard ground is assumed).

[ TUM ]

Autonomous driving when you cannot see the road at all because it's buried in snow is some serious autonomous driving.

[ Norlab ]

A hierarchical and robust framework for learning bipedal locomotion is presented and successfully implemented on the 3D biped robot Digit. The feasibility of the method is demonstrated by successfully transferring the learned policy in simulation to the Digit robot hardware, realizing sustained walking gaits under external force disturbances and challenging terrains not included during the training process.

[ OSU ]

This is a video summary of the Center for Robot-Assisted Search and Rescue's deployments under the direction of emergency response agencies to more than 30 disasters in five countries from 2001 (9/11 World Trade Center) to 2018 (Hurricane Michael). It includes the first use of ground robots for a disaster (WTC, 2001), the first use of small unmanned aerial systems (Hurricane Katrina 2005), and the first use of water surface vehicles (Hurricane Wilma, 2005).

[ CRASAR ]

In March, a team from the Oxford Robotics Institute collected a week of epic off-road driving data, as part of the Sense-Assess-eXplain (SAX) project.

[ Oxford Robotics ]

As a part of the AAAI 2021 Spring Symposium Series, HEBI Robotics was invited to present an Industry Talk on the symposium's topic: Machine Learning for Mobile Robot Navigation in the Wild. Included in this presentation was a short case study on one of our upcoming mobile robots that is being designed to successfully navigate unstructured environments where today's robots struggle.

[ HEBI Robotics ]

Thanks Hardik!

This Lockheed Martin Robotics Seminar is from Chad Jenkins at the University of Michigan, on “Semantic Robot Programming… and Maybe Making the World a Better Place.”

I will present our efforts towards accessible and general methods of robot programming from the demonstrations of human users. Our recent work has focused on Semantic Robot Programming (SRP), a declarative paradigm for robot programming by demonstration that builds on semantic mapping. In contrast to procedural methods for motion imitation in configuration space, SRP is suited to generalize user demonstrations of goal scenes in workspace, such as for manipulation in cluttered environments. SRP extends our efforts to crowdsource robot learning from demonstration at scale through messaging protocols suited to web/cloud robotics. With such scaling of robotics in mind, prospects for cultivating both equal opportunity and technological excellence will be discussed in the context of broadening and strengthening Title IX and Title VI.

[ UMD ] Continue reading

Posted in Human Robots

#439073 There’s a ‘New’ Nirvana Song Out, ...

One of the primary capabilities separating human intelligence from artificial intelligence is our ability to be creative—to use nothing but the world around us, our experiences, and our brains to create art. At present, AI needs to be extensively trained on human-made works of art in order to produce new work, so we’ve still got a leg up. That said, neural networks like OpenAI’s GPT-3 and Russian designer Nikolay Ironov have been able to create content indistinguishable from human-made work.

Now there’s another example of AI artistry that’s hard to tell apart from the real thing, and it’s sure to excite 90s alternative rock fans the world over: a brand-new, never-heard-before Nirvana song. Or, more accurately, a song written by a neural network that was trained on Nirvana’s music.

The song is called “Drowned in the Sun,” and it does have a pretty Nirvana-esque ring to it. The neural network that wrote it is Magenta, which was launched by Google in 2016 with the goal of training machines to create art—or as the tool’s website puts it, exploring the role of machine learning as a tool in the creative process. Magenta was built using TensorFlow, Google’s massive open-source software library focused on deep learning applications.

The song was written as part of an album called Lost Tapes of the 27 Club, a project carried out by a Toronto-based organization called Over the Bridge focused on mental health in the music industry.

Here’s how a computer was able to write a song in the unique style of a deceased musician. Music, 20 to 30 tracks, was fed into Magenta’s neural network in the form of MIDI files. MIDI stands for Musical Instrument Digital Interface, and the format contains the details of a song written in code that represents musical parameters like pitch and tempo. Components of each song, like vocal melody or rhythm guitar, were fed in one at a time.

The neural network found patterns in these different components, and got enough of a handle on them that when given a few notes to start from, it could use those patterns to predict what would come next; in this case, chords and melodies that sound like they could’ve been written by Kurt Cobain.

To be clear, Magenta didn’t spit out a ready-to-go song complete with lyrics. The AI wrote the music, but a different neural network wrote the lyrics (using essentially the same process as Magenta), and the team then sifted through “pages and pages” of output to find lyrics that fit the melodies Magenta created.

Eric Hogan, a singer for a Nirvana tribute band who the Over the Bridge team hired to sing “Drowned in the Sun,” felt that the lyrics were spot-on. “The song is saying, ‘I’m a weirdo, but I like it,’” he said. “That is total Kurt Cobain right there. The sentiment is exactly what he would have said.”

Cobain isn’t the only musician the Lost Tapes project tried to emulate; songs in the styles of Jimi Hendrix, Jim Morrison, and Amy Winehouse were also included. What all these artists have in common is that they died by suicide at the age of 27.

The project is meant to raise awareness around mental health, particularly among music industry professionals. It’s not hard to think of great artists of all persuasions—musicians, painters, writers, actors—whose lives are cut short due to severe depression and other mental health issues for which it can be hard to get help. These issues are sometimes romanticized, as suffering does tend to create art that’s meaningful, relatable, and timeless. But according to the Lost Tapes website, suicide attempts among music industry workers are more than double that of the general population.

How many more hit songs would these artists have written if they were still alive? We’ll never know, but hopefully Lost Tapes of the 27 Club and projects like it will raise awareness of mental health issues, both in the music industry and in general, and help people in need find the right resources. Because no matter how good computers eventually get at creating music, writing, or other art, as Lost Tapes’ website pointedly says, “Even AI will never replace the real thing.”

Image Credit: Edward Xu on Unsplash Continue reading

Posted in Human Robots

#439042 How Scientists Used Ultrasound to Read ...

Thanks to neural implants, mind reading is no longer science fiction.

As I’m writing this sentence, a tiny chip with arrays of electrodes could sit on my brain, listening in on the crackling of my neurons firing as my hands dance across the keyboard. Sophisticated algorithms could then decode these electrical signals in real time. My brain’s inner language to plan and move my fingers could then be used to guide a robotic hand to do the same. Mind-to-machine control, voilà!

Yet as the name implies, even the most advanced neural implant has a problem: it’s an implant. For electrodes to reliably read the brain’s electrical chatter, they need to pierce through the its protective membrane and into brain tissue. Danger of infection aside, over time, damage accumulates around the electrodes, distorting their signals or even rendering them unusable.

Now, researchers from Caltech have paved a way to read the brain without any physical contact. Key to their device is a relatively new superstar in neuroscience: functional ultrasound, which uses sound waves to capture activity in the brain.

In monkeys, the technology could reliably predict their eye movement and hand gestures after just a single trial—without the usual lengthy training process needed to decode a movement. If adopted by humans, the new mind-reading tech represents a triple triumph: it requires minimal surgery and minimal learning, but yields maximal resolution for brain decoding. For people who are paralyzed, it could be a paradigm shift in how they control their prosthetics.

“We pushed the limits of ultrasound neuroimaging and were thrilled that it could predict movement,” said study author Dr. Sumner Norman.

To Dr. Krishna Shenoy at Stanford, who was not involved, the study will finally put ultrasound “on the map as a brain-machine interface technique. Adding to this toolkit is spectacular,” he said.

Breaking the Sound Barrier
Using sound to decode brain activity might seem preposterous, but ultrasound has had quite the run in medicine. You’ve probably heard of its most common use: taking photos of a fetus in pregnancy. The technique uses a transducer, which emits ultrasound pulses into the body and finds boundaries in tissue structure by analyzing the sound waves that bounce back.

Roughly a decade ago, neuroscientists realized they could adapt the tech for brain scanning. Rather than directly measuring the brain’s electrical chatter, it looks at a proxy—blood flow. When certain brain regions or circuits are active, the brain requires much more energy, which is provided by increased blood flow. In this way, functional ultrasound works similarly to functional MRI, but at a far higher resolution—roughly ten times, the authors said. Plus, people don’t have to lie very still in an expensive, claustrophobic magnet.

“A key question in this work was: If we have a technique like functional ultrasound that gives us high-resolution images of the brain’s blood flow dynamics in space and over time, is there enough information from that imaging to decode something useful about behavior?” said study author Dr. Mikhail Shapiro.

There’s plenty of reasons for doubt. As the new kid on the block, functional ultrasound has some known drawbacks. A major one: it gives a far less direct signal than electrodes. Previous studies show that, with multiple measurements, it can provide a rough picture of brain activity. But is that enough detail to guide a robotic prosthesis?

One-Trial Wonder
The new study put functional ultrasound to the ultimate test: could it reliably detect movement intention in monkeys? Because their brains are the most similar to ours, rhesus macaque monkeys are often the critical step before a brain-machine interface technology is adapted for humans.

The team first inserted small ultrasound transducers into the skulls of two rhesus monkeys. While it sounds intense, the surgery doesn’t penetrate the brain or its protective membrane; it’s only on the skull. Compared to electrodes, this means the brain itself isn’t physically harmed.

The device is linked to a computer, which controls the direction of sound waves and captures signals from the brain. For this study, the team aimed the pulses at the posterior parietal cortex, a part of the “motor” aspect of the brain, which plans movement. If right now you’re thinking about scrolling down this page, that’s the brain region already activated, before your fingers actually perform the movement.

Then came the tests. The first looked at eye movements—something pretty necessary before planning actual body movements without tripping all over the place. Here, the monkeys learned to focus on a central dot on a computer screen. A second dot, either left or right, then flashed. The monkeys’ task was to flicker their eyes to the most recent dot. It’s something that seems easy for us, but requires sophisticated brain computation.

The second task was more straightforward. Rather than just moving their eyes to the second target dot, the monkeys learned to grab and manipulate a joystick to move a cursor to that target.

Using brain imaging to decode the mind and control movement. Image Credit: S. Norman, Caltech
As the monkeys learned, so did the device. Ultrasound data capturing brain activity was fed into a sophisticated machine learning algorithm to guess the monkeys’ intentions. Here’s the kicker: once trained, using data from just a single trial, the algorithm was able to correctly predict the monkeys’ actual eye movement—whether left or right—with roughly 78 percent accuracy. The accuracy for correctly maneuvering the joystick was even higher, at nearly 90 percent.

That’s crazy accurate, and very much needed for a mind-controlled prosthetic. If you’re using a mind-controlled cursor or limb, the last thing you’d want is to have to imagine the movement multiple times before you actually click the web button, grab the door handle, or move your robotic leg.

Even more impressive is the resolution. Sound waves seem omnipresent, but with focused ultrasound, it’s possible to measure brain activity at a resolution of 100 microns—roughly 10 neurons in the brain.

A Cyborg Future?
Before you start worrying about scientists blasting your brain with sound waves to hack your mind, don’t worry. The new tech still requires skull surgery, meaning that a small chunk of skull needs to be removed. However, the brain itself is spared. This means that compared to electrodes, ultrasound could offer less damage and potentially a far longer mind reading than anything currently possible.

There are downsides. Focused ultrasound is far younger than any electrode-based neural implants, and can’t yet reliably decode 360-degree movement or fine finger movements. For now, the tech requires a wire to link the device to a computer, which is off-putting to many people and will prevent widespread adoption. Add to that the inherent downside of focused ultrasound, which lags behind electrical recordings by roughly two seconds.

All that aside, however, the tech is just tiptoeing into a future where minds and machines seamlessly connect. Ultrasound can penetrate the skull, though not yet at the resolution needed for imaging and decoding brain activity. The team is already working with human volunteers with traumatic brain injuries, who had to have a piece of their skulls removed, to see how well ultrasound works for reading their minds.

“What’s most exciting is that functional ultrasound is a young technique with huge potential. This is just our first step in bringing high performance, less invasive brain-machine interface to more people,” said Norman.

Image Credit: Free-Photos / Pixabay Continue reading

Posted in Human Robots