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#436466 How Two Robots Learned to Grill and ...

The list of things robots can do seems to be growing by the week. They can play sports, help us explore outer space and the deep sea, take over some of our boring everyday tasks, and even assemble Ikea furniture.

Now they can add one more accomplishment to the list: grilling and serving a hot dog.

It seems like a pretty straightforward task, and as far as grilling goes, hot dogs are about as easy as it gets (along with, maybe, burgers? Hot dogs require more rotation, but it’s easier to tell when they’re done since they’re lighter in color).

Let’s paint a picture: you’re manning the grill at your family’s annual Fourth of July celebration. You’ve got a 10-pack of plump, juicy beef franks and a hungry crowd of relatives whose food-to-alcohol ratio is getting pretty skewed—they need some solid calories, pronto. What are the steps you need to take to get those franks from package to plate?

Each one needs to be placed on the grill, rotated every couple minutes for even cooking, removed from the grill when you deem it’s done, then—if you’re the kind of guy or gal who goes the extra mile—placed in a bun and dressed with ketchup, mustard, pickles, and the like before being handed over to salivating, too-loud Uncle Hector or sweet, bored Cousin Margaret.

While carrying out your grillmaster duties, you know better than to drop the hot dogs on the ground, leave them cooking on one side for too long, squeeze them to the point of breaking or bursting, and any other hot-dog-ruining amateur moves.

But for a robot, that’s a lot to figure out, especially if they have no prior knowledge of grilling hot dogs (which, well, most robots don’t).

As described in a paper published in this week’s Science Robotics, a team from Boston University programmed two robotic arms to use reinforcement learning—a branch of machine learning in which software gathers information about its environment then learns from it by replaying its experiences and incorporating rewards—to cook and serve hot dogs.

The team used a set of formulas to specify and combine tasks (“pick up hot dog and place on the grill”), meet safety requirements (“always avoid collisions”), and incorporate general prior knowledge (“you cannot pick up another hot dog if you are already holding one”).

Baxter and Jaco—as the two robots were dubbed—were trained through computer simulations. The paper’s authors emphasized their use of what they call a “formal specification language” for training the software, with the aim of generating easily-interpretable task descriptions. In reinforcement learning, they explain, being able to understand how a reward function influences an AI’s learning process is a key component in understanding the system’s behavior—but most systems lack this quality, and are thus likely to be lumped into the ‘black box’ of AI.

The robots’ decisions throughout the hot dog prep process—when to turn a hot dog, when to take it off the grill, and so on—are, the authors write, “easily interpretable from the beginning because the language is very similar to plain English.”

Besides being a step towards more explainable AI systems, Baxter and Jaco are another example of fast-food robots—following in the footsteps of their burger and pizza counterparts—that may take over some repetitive manual tasks currently performed by human workers. As robots’ capabilities improve through incremental progress like this, they’ll be able to take on additional tasks.

In a not-so-distant future, then, you just may find yourself throwing back drinks with Uncle Hector and Cousin Margaret while your robotic replacement mans the grill, churning out hot dogs that are perfectly cooked every time.

Image Credit: Image by Muhammad Ribkhan from Pixabay Continue reading

Posted in Human Robots

#436462 Robotic Exoskeletons, Like This One, Are ...

When you imagine an exoskeleton, chances are it might look a bit like the Guardian XO from Sarcos Robotics. The XO is literally a robot you wear (or maybe, it wears you). The suit’s powered limbs sense your movements and match their position to yours with little latency to give you effortless superstrength and endurance—lifting 200 pounds will feel like 10.

A vision of robots and humankind working together in harmony. Now, isn’t that nice?

Of course, there isn’t anything terribly novel about an exoskeleton. We’ve seen plenty of concepts and demonstrations in the last decade. These include light exoskeletons tailored to industrial settings—some of which are being tested out by the likes of Honda—and healthcare exoskeletons that support the elderly or folks with disabilities.

Full-body powered robotic exoskeletons are a bit rarer, which makes the Sarcos suit pretty cool to look at. But like all things in robotics, practicality matters as much as vision. It’s worth asking: Will anyone buy and use the thing? Is it more than a concept video?

Sarcos thinks so, and they’re excited about it. “If you were to ask the question, what does 30 years and $300 million look like,” Sarcos CEO, Ben Wolff, told IEEE Spectrum, “you’re going to see it downstairs.”

The XO appears to check a few key boxes. For one, it’s user friendly. According to Sarcos, it only takes a few minutes for the uninitiated to strap in and get up to speed. Feeling comfortable doing work with the suit takes a few hours. This is thanks to a high degree of sensor-based automation that allows the robot to seamlessly match its user’s movements.

The XO can also operate for more than a few minutes. It has two hours of battery life, and with spares on hand, it can go all day. The batteries are hot-swappable, meaning you can replace a drained battery with a new one without shutting the system down.

The suit is aimed at manufacturing, where workers are regularly moving heavy stuff around. Additionally, Wolff told CNET, the suit could see military use. But that doesn’t mean Avatar-style combat. The XO, Wolff said, is primarily about logistics (lifting and moving heavy loads) and isn’t designed to be armored, so it won’t likely see the front lines.

The system will set customers back $100,000 a year to rent, which sounds like a lot, but for industrial or military purposes, the six-figure rental may not deter would-be customers if the suit proves itself a useful bit of equipment. (And it’s reasonable to imagine the price coming down as the technology becomes more commonplace and competitors arrive.)

Sarcos got into exoskeletons a couple decades ago and was originally funded by the military (like many robotics endeavors). Videos hit YouTube as long ago as 2008, but after announcing the company was taking orders for the XO earlier this year, Sarcos says they’ll deliver the first alpha units in January, which is a notable milestone.

Broadly, robotics has advanced a lot in recent years. YouTube sensations like Boston Dynamics have regularly earned millions of views (and inevitably, headlines stoking robot fear). They went from tethered treadmill sessions to untethered backflips off boxes. While today’s robots really are vastly superior to their ancestors, they’ve struggled to prove themselves useful. A counterpoint to flashy YouTube videos, the DARPA Robotics Challenge gave birth to another meme altogether. Robots falling over. Often and awkwardly.

This year marks some of the first commercial fruits of a few decades’ research. Boston Dynamics recently started offering its robot dog, Spot, to select customers in 2019. Whether this proves to be a headline-worthy flash in the pan or something sustainable remains to be seen. But between robots with more autonomy and exoskeletons like the XO, the exoskeleton variety will likely be easier to make more practical for various uses.

Whereas autonomous robots require highly advanced automation to navigate uncertain and ever-changing conditions—automation which, at the moment, remains largely elusive (though the likes of Google are pairing the latest AI with robots to tackle the problem)—an exoskeleton mainly requires physical automation. The really hard bits, like navigating and recognizing and interacting with objects, are outsourced to its human operator.

As it turns out, for today’s robots the best AI is still us. We may yet get chipper automatons like Rosy the Robot, but until then, for complicated applications, we’ll strap into our mechs for their strength and endurance, and they’ll wear us for our brains.

Image Credit: Sarcos Robotics Continue reading

Posted in Human Robots

#436437 Why AI Will Be the Best Tool for ...

Dmitry Kaminskiy speaks as though he were trying to unload everything he knows about the science and economics of longevity—from senolytics research that seeks to stop aging cells from spewing inflammatory proteins and other molecules to the trillion-dollar life extension industry that he and his colleagues are trying to foster—in one sitting.

At the heart of the discussion with Singularity Hub is the idea that artificial intelligence will be the engine that drives breakthroughs in how we approach healthcare and healthy aging—a concept with little traction even just five years ago.

“At that time, it was considered too futuristic that artificial intelligence and data science … might be more accurate compared to any hypothesis of human doctors,” said Kaminskiy, co-founder and managing partner at Deep Knowledge Ventures, an investment firm that is betting big on AI and longevity.

How times have changed. Artificial intelligence in healthcare is attracting more investments and deals than just about any sector of the economy, according to data research firm CB Insights. In the most recent third quarter, AI healthcare startups raised nearly $1.6 billion, buoyed by a $550 million mega-round from London-based Babylon Health, which uses AI to collect data from patients, analyze the information, find comparable matches, then make recommendations.

Even without the big bump from Babylon Health, AI healthcare startups raised more than $1 billion last quarter, including two companies focused on longevity therapeutics: Juvenescence and Insilico Medicine.

The latter has risen to prominence for its novel use of reinforcement learning and general adversarial networks (GANs) to accelerate the drug discovery process. Insilico Medicine recently published a seminal paper that demonstrated how such an AI system could generate a drug candidate in just 46 days. Co-founder and CEO Alex Zhavoronkov said he believes there is no greater goal in healthcare today—or, really, any venture—than extending the healthy years of the human lifespan.

“I don’t think that there is anything more important than that,” he told Singularity Hub, explaining that an unhealthy society is detrimental to a healthy economy. “I think that it’s very, very important to extend healthy, productive lifespan just to fix the economy.”

An Aging Crisis
The surge of interest in longevity is coming at a time when life expectancy in the US is actually dropping, despite the fact that we spend more money on healthcare than any other nation.

A new paper in the Journal of the American Medical Association found that after six decades of gains, life expectancy for Americans has decreased since 2014, particularly among young and middle-aged adults. While some of the causes are societal, such as drug overdoses and suicide, others are health-related.

While average life expectancy in the US is 78, Kaminskiy noted that healthy life expectancy is about ten years less.

To Zhavoronkov’s point about the economy (a topic of great interest to Kaminskiy as well), the US spent $1.1 trillion on chronic diseases in 2016, according to a report from the Milken Institute, with diabetes, cardiovascular conditions, and Alzheimer’s among the most costly expenses to the healthcare system. When the indirect costs of lost economic productivity are included, the total price tag of chronic diseases in the US is $3.7 trillion, nearly 20 percent of GDP.

“So this is the major negative feedback on the national economy and creating a lot of negative social [and] financial issues,” Kaminskiy said.

Investing in Longevity
That has convinced Kaminskiy that an economy focused on extending healthy human lifespans—including the financial instruments and institutions required to support a long-lived population—is the best way forward.

He has co-authored a book on the topic with Margaretta Colangelo, another managing partner at Deep Knowledge Ventures, which has launched a specialized investment fund, Longevity.Capital, focused on the longevity industry. Kaminskiy estimates that there are now about 20 such investment funds dedicated to funding life extension companies.

In November at the inaugural AI for Longevity Summit in London, he and his collaborators also introduced the Longevity AI Consortium, an academic-industry initiative at King’s College London. Eventually, the research center will include an AI Longevity Accelerator program to serve as a bridge between startups and UK investors.

Deep Knowledge Ventures has committed about £7 million ($9 million) over the next three years to the accelerator program, as well as establishing similar consortiums in other regions of the world, according to Franco Cortese, a partner at Longevity.Capital and director of the Aging Analytics Agency, which has produced a series of reports on longevity.

A Cure for What Ages You
One of the most recent is an overview of Biomarkers for Longevity. A biomarker, in the case of longevity, is a measurable component of health that can indicate a disease state or a more general decline in health associated with aging. Examples range from something as simple as BMI as an indicator of obesity, which is associated with a number of chronic diseases, to sophisticated measurements of telomeres, the protective ends of chromosomes that shorten as we age.

While some researchers are working on moonshot therapies to reverse or slow aging—with a few even arguing we could expand human life on the order of centuries—Kaminskiy said he believes understanding biomarkers of aging could make more radical interventions unnecessary.

In this vision of healthcare, people would be able to monitor their health 24-7, with sensors attuned to various biomarkers that could indicate the onset of everything from the flu to diabetes. AI would be instrumental in not just ingesting the billions of data points required to develop such a system, but also what therapies, treatments, or micro-doses of a drug or supplement would be required to maintain homeostasis.

“Consider it like Tesla with many, many detectors, analyzing the behavior of the car in real time, and a cloud computing system monitoring those signals in real time with high frequency,” Kaminskiy explained. “So the same shall be applied for humans.”

And only sophisticated algorithms, Kaminskiy argued, can make longevity healthcare work on a mass scale but at the individual level. Precision medicine becomes preventive medicine. Healthcare truly becomes a system to support health rather than a way to fight disease.

Image Credit: Photo by h heyerlein on Unsplash Continue reading

Posted in Human Robots

#436426 Video Friday: This Robot Refuses to Fall ...

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!):

Robotic Arena – January 25, 2020 – Wrocław, Poland
DARPA SubT Urban Circuit – February 18-27, 2020 – Olympia, Wash., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

In case you somehow missed the massive Skydio 2 review we posted earlier this week, the first batches of the drone are now shipping. Each drone gets a lot of attention before it goes out the door, and here’s a behind-the-scenes clip of the process.

[ Skydio ]

Sphero RVR is one of the 15 robots on our robot gift guide this year. Here’s a new video Sphero just released showing some of the things you can do with the robot.

[ RVR ]

NimbRo-OP2 has some impressive recovery skills from the obligatory research-motivated robot abuse.

[ NimbRo ]

Teams seeking to qualify for the Virtual Urban Circuit of the Subterranean Challenge can access practice worlds to test their approaches prior to submitting solutions for the competition. This video previews three of the practice environments.

[ DARPA SubT ]

Stretchable skin-like robots that can be rolled up and put in your pocket have been developed by a University of Bristol team using a new way of embedding artificial muscles and electrical adhesion into soft materials.

[ Bristol ]

Happy Holidays from ABB!

Helping New York celebrate the festive season, twelve ABB robots are interacting with visitors to Bloomingdale’s iconic holiday celebration at their 59th Street flagship store. ABB’s robots are the main attraction in three of Bloomingdale’s twelve-holiday window displays at Lexington and Third Avenue, as ABB demonstrates the potential for its robotics and automation technology to revolutionize visual merchandising and make the retail experience more dynamic and whimsical.

[ ABB ]

We introduce pelican eel–inspired dual-morphing architectures that embody quasi-sequential behaviors of origami unfolding and skin stretching in response to fluid pressure. In the proposed system, fluid paths were enclosed and guided by a set of entirely stretchable origami units that imitate the morphing principle of the pelican eel’s stretchable and foldable frames. This geometric and elastomeric design of fluid networks, in which fluid pressure acts in the direction that the whole body deploys first, resulted in a quasi-sequential dual-morphing response. To verify the effectiveness of our design rule, we built an artificial creature mimicking a pelican eel and reproduced biomimetic dual-morphing behavior.

And here’s a real pelican eel:

[ Science Robotics ]

Delft Dynamics’ updated anti-drone system involves a tether, mid-air net gun, and even a parachute.

[ Delft Dynamics ]

Teleoperation is a great way of helping robots with complex tasks, especially if you can do it through motion capture. But what if you’re teleoperating a non-anthropomorphic robot? Columbia’s ROAM Lab is working on it.

[ Paper ] via [ ROAM Lab ]

I don’t know how I missed this video last year because it’s got a steely robot hand squeezing a cute lil’ chick.

[ MotionLib ] via [ RobotStart ]

In this video we present results of a trajectory generation method for autonomous overtaking of unexpected obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example when overtaking unexpected objects on the vehicle’s ego lane on a two-way street. In this case, a human driver would first make sure that the opposite lane is free and that there is enough room to successfully execute the maneuver, and then it would cut into the opposite lane in order to execute the maneuver successfully. We consider the practical problem of autonomous overtaking when the coverage of the perception system is impaired due to occlusion.

[ Paper ]

New weirdness from Toio!

[ Toio ]

Palo Alto City Library won a technology innovation award! Watch to see how Senior Librarian Dan Lou is using Misty to enhance their technology programs to inspire and educate customers.

[ Misty Robotics ]

We consider the problem of reorienting a rigid object with arbitrary known shape on a table using a two-finger pinch gripper. Reorienting problem is challenging because of its non-smoothness and high dimensionality. In this work, we focus on solving reorienting using pivoting, in which we allow the grasped object to rotate between fingers. Pivoting decouples the gripper rotation from the object motion, making it possible to reorient an object under strict robot workspace constraints.

[ CMU ]

How can a mobile robot be a good pedestrian without bumping into you on the sidewalk? It must be hard for a robot to navigate in crowded environments since the flow of traffic follows implied social rules. But researchers from MIT developed an algorithm that teaches mobile robots to maneuver in crowds of people, respecting their natural behaviour.

[ Roboy Research Reviews ]

What happens when humans and robots make art together? In this awe-inspiring talk, artist Sougwen Chung shows how she “taught” her artistic style to a machine — and shares the results of their collaboration after making an unexpected discovery: robots make mistakes, too. “Part of the beauty of human and machine systems is their inherent, shared fallibility,” she says.

[ TED ]

Last month at the Cooper Union in New York City, IEEE TechEthics hosted a public panel session on the facts and misperceptions of autonomous vehicles, part of the IEEE TechEthics Conversations Series. The speakers were: Jason Borenstein from Georgia Tech; Missy Cummings from Duke University; Jack Pokrzywa from SAE; and Heather M. Roff from Johns Hopkins Applied Physics Laboratory. The panel was moderated by Mark A. Vasquez, program manager for IEEE TechEthics.

[ IEEE TechEthics ]

Two videos this week from Lex Fridman’s AI podcast: Noam Chomsky, and Whitney Cummings.

[ AI Podcast ]

This week’s CMU RI Seminar comes from Jeff Clune at the University of Wyoming, on “Improving Robot and Deep Reinforcement Learning via Quality Diversity and Open-Ended Algorithms.”

Quality Diversity (QD) algorithms are those that seek to produce a diverse set of high-performing solutions to problems. I will describe them and a number of their positive attributes. I will then summarize our Nature paper on how they, when combined with Bayesian Optimization, produce a learning algorithm that enables robots, after being damaged, to adapt in 1-2 minutes in order to continue performing their mission, yielding state-of-the-art robot damage recovery. I will next describe our QD-based Go-Explore algorithm, which dramatically improves the ability of deep reinforcement learning algorithms to solve previously unsolvable problems wherein reward signals are sparse, meaning that intelligent exploration is required. Go-Explore solves Montezuma’s Revenge, considered by many to be a major AI research challenge. Finally, I will motivate research into open-ended algorithms, which seek to innovate endlessly, and introduce our POET algorithm, which generates its own training challenges while learning to solve them, automatically creating a curricula for robots to learn an expanding set of diverse skills. POET creates and solves challenges that are unsolvable with traditional deep reinforcement learning techniques.

[ CMU RI ] Continue reading

Posted in Human Robots

#436414 Japanese Researchers Teaching Robots to ...

When mobile manipulators eventually make it into our homes, self-repair is going to be a very important function. Hopefully, these robots will be durable enough that they won’t need to be repaired very often, but from time to time they’ll almost certainly need minor maintenance. At Humanoids 2019 in Toronto, researchers from the University of Tokyo showed how they taught a PR2 to perform simple repairs on itself by tightening its own screws. And using that skill, the robot was also able to augment itself, adding accessories like hooks to help it carry more stuff. Clever robot!

To keep things simple, the researchers provided the robot with CAD data that tells it exactly where all of its screws are.

At the moment, the robot can’t directly detect on its own whether a particular screw needs tightening, although it can tell if its physical pose doesn’t match its digital model, which suggests that something has gone wonky. It can also check its screws autonomously from time to time, or rely on a human physically pointing out that it has a screw loose, using the human’s finger location to identify which screw it is. Another challenge is that most robots, like most humans, are limited in the areas on themselves that they can comfortably reach. So to tighten up everything, they might have to find themselves a robot friend to help, just like humans help each other put on sunblock.

The actual tightening is either super easy or quite complicated, depending on the location and orientation of the screw. If the robot is lucky, it can just use its continuous wrist rotation for tightening, but if a screw is located in a tight position that requires an Allen wrench, the robot has to regrasp the tool over and over as it incrementally tightens the screw.

Image: University of Tokyo

In one experiment, the researchers taught a PR2 robot to attach a hook to one of its shoulders. The robot uses one hand to grasp the hook and another hand to grasp a screwdriver. The researchers tested the hook by hanging a tote bag on it.

The other neat trick that a robot can do once it can tighten screws on its own body is to add new bits of hardware to itself. PR2 was thoughtfully designed with mounting points on its shoulders (or maybe technically its neck) and head, and it turns out that it can reach these points with its manipulators, allowing to modify itself, as the researchers explain:

When PR2 wants to have a lot of things, the only two hands are not enough to realize that. So we let PR2 to use a bag the same as we put it on our shoulder. PR2 started attaching the hook whose pose is calculated with self CAD data with a driver on his shoulder in order to put a bag on his shoulder. PR2 finished attaching the hook, and the people put a lot of cans in a tote bag and put it on PR2’s shoulder.

“Self-Repair and Self-Extension by Tightening Screws based on Precise Calculation of Screw Pose of Self-Body with CAD Data and Graph Search with Regrasping a Driver,” by Takayuki Murooka, Kei Okada, and Masayuki Inaba from the University of Tokyo, was presented at Humanoids 2019 in Toronto, Canada. Continue reading

Posted in Human Robots