Tag Archives: effects
#436403 Why Your 5G Phone Connection Could Mean ...
Will getting full bars on your 5G connection mean getting caught out by sudden weather changes?
The question may strike you as hypothetical, nonsensical even, but it is at the core of ongoing disputes between meteorologists and telecommunications companies. Everyone else, including you and I, are caught in the middle, wanting both 5G’s faster connection speeds and precise information about our increasingly unpredictable weather. So why can’t we have both?
Perhaps we can, but because of the way 5G networks function, it may take some special technology—specifically, artificial intelligence.
The Bandwidth Worries
Around the world, the first 5G networks are already being rolled out. The networks use a variety of frequencies to transmit data to and from devices at speeds up to 100 times faster than existing 4G networks.
One of the bandwidths used is between 24.25 and 24.45 gigahertz (GHz). In a recent FCC auction, telecommunications companies paid a combined $2 billion for the 5G usage rights for this spectrum in the US.
However, meteorologists are concerned that transmissions near the lower end of that range can interfere with their ability to accurately measure water vapor in the atmosphere. Wired reported that acting chief of the National Oceanic and Atmospheric Administration (NOAA), Neil Jacobs, told the US House Subcommittee on the Environment that 5G interference could substantially cut the amount of weather data satellites can gather. As a result, forecast accuracy could drop by as much as 30 percent.
Among the consequences could be less time to prepare for hurricanes, and it may become harder to predict storms’ paths. Due to the interconnectedness of weather patterns, measurement issues in one location can affect other areas too. Lack of accurate atmospheric data from the US could, for example, lead to less accurate forecasts for weather patterns over Europe.
The Numbers Game
Water vapor emits a faint signal at 23.8 GHz. Weather satellites measure the signals, and the data is used to gauge atmospheric humidity levels. Meteorologists have expressed concern that 5G signals in the same range can disturb those readings. The issue is that it would be nigh on impossible to tell whether a signal is water vapor or an errant 5G signal.
Furthermore, 5G disturbances in other frequency bands could make forecasting even more difficult. Rain and snow emit frequencies around 36-37 GHz. 50.2-50.4 GHz is used to measure atmospheric temperatures, and 86-92 GHz clouds and ice. All of the above are under consideration for international 5G signals. Some have warned that the wider consequences could set weather forecasts back to the 1980s.
Telecommunications companies and interest organizations have argued back, saying that weather sensors aren’t as susceptible to interference as meteorologists fear. Furthermore, 5G devices and signals will produce much less interference with weather forecasts than organizations like NOAA predict. Since very little scientific research has been carried out to examine the claims of either party, we seem stuck in a ‘wait and see’ situation.
To offset some of the possible effects, the two groups have tried to reach a consensus on a noise buffer between the 5G transmissions and water-vapor signals. It could be likened to limiting the noise from busy roads or loud sound systems to avoid bothering neighboring buildings.
The World Meteorological Organization was looking to establish a -55 decibel watts buffer. In Europe, regulators are locked in on a -42 decibel watts buffer for 5G base stations. For comparison, the US Federal Communications Commission has advocated for a -20 decibel watts buffer, which would, in reality, allow more than 150 times more noise than the European proposal.
How AI Could Help
Much of the conversation about 5G’s possible influence on future weather predictions is centered around mobile phones. However, the phones are far from the only systems that will be receiving and transmitting signals on 5G. Self-driving cars and the Internet of Things are two other technologies that could soon be heavily reliant on faster wireless signals.
Densely populated areas are likely going to be the biggest emitters of 5G signals, leading to a suggestion to only gather water-vapor data over oceans.
Another option is to develop artificial intelligence (AI) approaches to clean or process weather data. AI is playing an increasing role in weather forecasting. For example, in 2016 IBM bought The Weather Company for $2 billion. The goal was to combine the two companies’ models and data in IBM’s Watson to create more accurate forecasts. AI would also be able to predict increases or drops in business revenues due to weather changes. Monsanto has also been investing in AI for forecasting, in this case to provide agriculturally-related weather predictions.
Smartphones may also provide a piece of the weather forecasting puzzle. Studies have shown how data from thousands of smartphones can help to increase the accuracy of storm predictions, as well as the force of storms.
“Weather stations cost a lot of money,” Cliff Mass, an atmospheric scientist at the University of Washington in Seattle, told Inside Science, adding, “If there are already 20 million smartphones, you might as well take advantage of the observation system that’s already in place.”
Smartphones may not be the solution when it comes to finding new ways of gathering the atmospheric data on water vapor that 5G could disrupt. But it does go to show that some technologies open new doors, while at the same time, others shut them.
Image Credit: Image by Free-Photos from Pixabay Continue reading
Posted in Human Robots
#436180 Bipedal Robot Cassie Cal Learns to ...
There’s no particular reason why knowing how to juggle would be a useful skill for a robot. Despite this, robots are frequently taught how to juggle things. Blind robots can juggle, humanoid robots can juggle, and even drones can juggle. Why? Because juggling is hard, man! You have to think about a bunch of different things at once, and also do a bunch of different things at once, which this particular human at least finds to be overly stressful. While juggling may not stress robots out, it does require carefully coordinated sensing and computing and actuation, which means that it’s as good a task as any (and a more entertaining task than most) for testing the capabilities of your system.
UC Berkeley’s Cassie Cal robot, which consists of two legs and what could be called a torso if you were feeling charitable, has just learned to juggle by bouncing a ball on what would be her head if she had one of those. The idea is that if Cassie can juggle while balancing at the same time, she’ll be better able to do other things that require dynamic multitasking, too. And if that doesn’t work out, she’ll still be able to join the circus.
Cassie’s juggling is assisted by an external motion capture system that tracks the location of the ball, but otherwise everything is autonomous. Cassie is able to juggle the ball by leaning forwards and backwards, left and right, and moving up and down. She does this while maintaining her own balance, which is the whole point of this research—successfully executing two dynamic behaviors that may sometimes be at odds with one another. The end goal here is not to make a better juggling robot, but rather to explore dynamic multitasking, a skill that robots will need in order to be successful in human environments.
This work is from the Hybrid Robotics Lab at UC Berkeley, led by Koushil Sreenath, and is being done by Katherine Poggensee, Albert Li, Daniel Sotsaikich, Bike Zhang, and Prasanth Kotaru.
For a bit more detail, we spoke with Albert Li via email.
Image: UC Berkeley
UC Berkeley’s Cassie Cal getting ready to juggle.
IEEE Spectrum: What would be involved in getting Cassie to juggle without relying on motion capture?
Albert Li: Our motivation for starting off with motion capture was to first address the control challenge of juggling on a biped without worrying about implementing the perception. We actually do have a ball detector working on a camera, which would mean we wouldn’t have to rely on the motion capture system. However, we need to mount the camera in a way that it would provide the best upwards field of view, and we also have develop a reliable estimator. The estimator is particularly important because when the ball gets close enough to the camera, we actually can’t track the ball and have to assume our dynamic models describe its motion accurately enough until it bounces back up.
What keeps Cassie from juggling indefinitely?
There are a few factors that affect how long Cassie can sustain a juggle. While in simulation the paddle exhibits homogeneous properties like its stiffness and damping, in reality every surface has anisotropic contact properties. So, there are parts of the paddle which may be better for juggling than others (and importantly, react differently than modeled). These differences in contact are also exacerbated due to how the paddle is cantilevered when mounted on Cassie. When the ball hits these areas, it leads to a larger than expected error in a juggle. Due to the small size of the paddle, the ball may then just hit the paddle’s edge and end the juggling run. Over a very long run, this is a likely occurrence. Additionally, some large juggling errors could cause Cassie’s feet to slip slightly, which ends up changing the stable standing position over time. Since this version of the controller assumes Cassie is stationary, this change in position eventually leads to poor juggles and failure.
Would Cassie be able to juggle while walking (or hovershoe-ing)?
Walking (and hovershoe-ing) while juggling is a far more challenging problem and is certainly a goal for future research. Some of these challenges include getting the paddle to precise poses to juggle the ball while also moving to avoid any destabilizing effects of stepping incorrectly. The number of juggles per step of walking could also vary and make the mathematics of the problem more challenging. The controller goal is also more involved. While the current goal of the juggling controller is to juggle the ball to a static apex position, with a walking juggling controller, we may instead want to hit the ball forwards and also walk forwards to bounce it, juggle the ball along a particular path, etc. Solving such challenges would be the main thrusts of the follow-up research.
Can you give an example of a practical task that would be made possible by using a controller like this?
Studying juggling means studying contact behavior and leveraging our models of it to achieve a known objective. Juggling could also be used to study predictable post-contact flight behavior. Consider the scenario where a robot is attempting to make a catch, but fails, letting the ball to bounce off of its hand, and then recovering the catch. This behavior could also be intentional: It is often easier to first execute a bounce to direct the target and then perform a subsequent action. For example, volleyball players could in principle directly hit a spiked ball back, but almost always bump the ball back up and then return it.
Even beyond this motivating example, the kinds of models we employ to get juggling working are more generally applicable to any task that involves contact, which could include tasks besides bouncing like sliding and rolling. For example, clearing space on a desk by pushing objects to the side may be preferable than individually manipulating each and every object on it.
You mention collaborative juggling or juggling multiple balls—is that something you’ve tried yet? Can you talk a bit more about what you’re working on next?
We haven’t yet started working on collaborative or multi-ball juggling, but that’s also a goal for future work. Juggling multiple balls statically is probably the most reasonable next goal, but presents additional challenges. For instance, you have to encode a notion of juggling urgency (if the second ball isn’t hit hard enough, you have less time to get the first ball up before you get back to the second one).
On the other hand, collaborative human-robot juggling requires a more advanced decision-making framework. To get robust multi-agent juggling, the robot will need to employ some sort of probabilistic model of the expected human behavior (are they likely to move somewhere? Are they trying to catch the ball high or low? Is it safe to hit the ball back?). In general, developing such human models is difficult since humans are fairly unpredictable and often don’t exhibit rational behavior. This will be a focus of future work.
[ Hybrid Robotics Lab ] Continue reading
Posted in Human Robots
#436167 Is it Time for Tech to Stop Moving Fast ...
On Monday, I attended the 2019 Fall Conference of Stanford’s Institute for Human Centered Artificial Intelligence (HAI). That same night I watched the Season 6 opener for the HBO TV show Silicon Valley. And the debates featured in both surrounded the responsibility of tech companies for the societal effects of the technologies they produce. The two events have jumbled together in my mind, perhaps because I was in a bit of a brain fog, thanks to the nasty combination of a head cold and the smoke that descended on Silicon Valley from the northern California wildfires. But perhaps that mixture turned out to be a good thing.
What is clear, in spite of the smoke, is that this issue is something a lot of people are talking about, inside and outside of Silicon Valley (witness the viral video of Rep. Alexandria Ocasio-Cortez (D-NY) grilling Facebook CEO Mark Zuckerberg).
So, to add to that conversation, here’s my HBO Silicon Valley/Stanford HAI conference mashup.
Silicon Valley’s fictional CEO Richard Hendriks, in the opening scene of the episode, tells Congress that Facebook, Google, and Amazon only care about exploiting personal data for profit. He states:
“These companies are kings, and they rule over kingdoms far larger than any nation in history.”
Meanwhile Marietje Schaake, former member of the European Parliament and a fellow at HAI, told the conference audience of 900:
“There is a lot of power in the hands of few actors—Facebook decides who is a news source, Microsoft will run the defense department’s cloud…. I believe we need a deeper debate about which tasks need to stay in the hands of the public.”
Eric Schmidt, former CEO and executive chairman of Google, agreed. He says:
“It is important that we debate now the ethics of what we are doing, and the impact of the technology that we are building.”
Stanford Associate Professor Ge Wang, also speaking at the HAI conference, pointed out:
“‘Doing no harm’ is a vital goal, and it is not easy. But it is different from a proactive goal, to ‘do good.’”
Had Silicon Valley’s Hendricks been there, he would have agreed. He said in the episode:
“Just because it’s successful, doesn’t mean it’s good. Hiroshima was a successful implementation.”
The speakers at the HAI conference discussed the implications of moving fast and breaking things, of putting untested and unregulated technology into the world now that we know that things like public trust and even democracy can be broken.
Google’s Schmidt told the HAI audience:
“I don’t think that everything that is possible should be put into the wild in society, we should answer the question, collectively, how much risk are we willing to take.
And Silicon Valley denizens real and fictional no longer think it’s OK to just say sorry afterwards. Says Schmidt:
“When you ask Facebook about various scandals, how can they still say ‘We are very sorry; we have a lot of learning to do.’ This kind of naiveté stands out of proportion to the power tech companies have. With great power should come great responsibility, or at least modesty.”
Schaake argued:
“We need more guarantees, institutions, and policies than stated good intentions. It’s about more than promises.”
Fictional CEO Hendricks thinks saying sorry is a cop-out as well. In the episode, a developer admits that his app collected user data in spite of Hendricks assuring Congress that his company doesn’t do that:
“You didn’t know at the time,” the developer says. “Don’t beat yourself up about it. But in the future, stop saying it. Or don’t; I don’t care. Maybe it will be like Google saying ‘Don’t be evil,’ or Facebook saying ‘I’m sorry, we’ll do better.’”
Hendricks doesn’t buy it:
“This stops now. I’m the boss, and this is over.”
(Well, he is fictional.)
How can government, the tech world, and the general public address this in a more comprehensive way? Out in the real world, the “what to do” discussion at Stanford HAI surrounded regulation—how much, what kind, and when.
Says the European Parliament’s Schaake:
“An often-heard argument is that government should refrain from regulating tech because [regulation] will stifle innovation. [That argument] implies that innovation is more important than democracy or the rule of law. Our problems don’t stem from over regulation, but under regulation of technologies.”
But when should that regulation happen. Stanford provost emeritus John Etchemendy, speaking from the audience at the HAI conference, said:
“I’ve been an advocate of not trying to regulate before you understand it. Like San Francisco banning of use of facial recognition is not a good example of regulation; there are uses of facial recognition that we should allow. We want regulations that are just right, that prevent the bad things and allow the good things. So we are going to get it wrong either way, if we regulate to soon or hold off, we will get some things wrong.”
Schaake would opt for regulating sooner rather than later. She says that she often hears the argument that it is too early to regulate artificial intelligence—as well as the argument that it is too late to regulate ad-based political advertising, or online privacy. Neither, to her, makes sense. She told the HAI attendees:
“We need more than guarantees than stated good intentions.”
U.S. Chief Technology Officer Michael Kratsios would go with later rather than sooner. (And, yes, the country has a CTO. President Barack Obama created the position in 2009; Kratsios is the fourth to hold the office and the first under President Donald Trump. He was confirmed in August.) Also speaking at the HAI conference, Kratsios argued:
“I don’t think we should be running to regulate anything. We are a leader [in technology] not because we had great regulations, but we have taken a free market approach. We have done great in driving innovation in technologies that are born free, like the Internet. Technologies born in captivity, like autonomous vehicles, lag behind.”
In the fictional world of HBO’s Silicon Valley, startup founder Hendricks has a solution—a technical one of course: the decentralized Internet. He tells Congress:
“The way we win is by creating a new, decentralized Internet, one where the behavior of companies like this will be impossible, forever. Where it is the users, not the kings, who have sovereign control over their data. I will help you build an Internet that is of the people, by the people, and for the people.”
(This is not a fictional concept, though it is a long way from wide use. Also called the decentralized Web, the concept takes the content on today’s Web and fragments it, and then replicates and scatters those fragments to hosts around the world, increasing privacy and reducing the ability of governments to restrict access.)
If neither regulation nor technology comes to make the world safe from the unforeseen effects of new technologies, there is one more hope, according to Schaake: the millennials and subsequent generations.
Tech companies can no longer pursue growth at all costs, not if they want to keep attracting the talent they need, says Schaake. She noted that, “the young generation looks at the environment, at homeless on the streets,” and they expect their companies to tackle those and other issues and make the world a better place. Continue reading
Posted in Human Robots
#436165 Video Friday: DJI’s Mavic Mini Is ...
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!):
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.
DJI’s new Mavic Mini looks like a pretty great drone for US $400 ($500 for a combo with more accessories): It’s tiny, flies for 30 minutes, and will do what you need as far as pictures and video (although not a whole lot more).
DJI seems to have put a bunch of effort into making the drone 249 grams, 1 gram under what’s required for FAA registration. That means you save $5 and a few minutes of your time, but that does not mean you don’t have to follow the FAA’s rules and regulations governing drone use.
[ DJI ]
Don’t panic, but Clearpath and HEBI Robotics have armed the Jackal:
After locking eyes across a crowded room at ICRA 2019, Clearpath Robotics and HEBI Robotics basked in that warm and fuzzy feeling that comes with starting a new and exciting relationship. Over a conference hall coffee, they learned that the two companies have many overlapping interests. The most compelling was the realization that customers across a variety of industries are hunting for an elusive true love of their own – a robust but compact robotic platform combined with a long reach manipulator for remote inspection tasks.
After ICRA concluded, Arron Griffiths, Application Engineer at Clearpath, and Matthew Tesch, Software Engineer at HEBI, kept in touch and decided there had been enough magic in the air to warrant further exploration. A couple of months later, Matthew arrived at Clearpath to formally introduce the HEBI’s X-Series Arm to Clearpath’s Jackal UGV. It was love.
[ Clearpath ]
Thanks Dave!
I’m really not a fan of the people-carrying drones, but heavy lift cargo drones seem like a more okay idea.
Volocopter, the pioneer in Urban Air Mobility, presented the demonstrator of its VoloDrone. This marks Volocopters expansion into the logistics, agriculture, infrastructure and public services industry. The VoloDrone is an unmanned, fully electric, heavy-lift utility drone capable of carrying a payload of 200 kg (440 lbs) up to 40 km (25 miles). With a standardized payload attachment, VoloDrone can serve a great variety of purposes from transporting boxes, to liquids, to equipment and beyond. It can be remotely piloted or flown in automated mode on pre-set routes.
[ Volocopter ]
JAY is a mobile service robot that projects a display on the floor and plays sound with its speaker. By playing sounds and videos, it provides visual and audio entertainment in various places such as exhibition halls, airports, hotels, department stores and more.
[ Rainbow Robotics ]
The DARPA Subterranean Challenge Virtual Tunnel Circuit concluded this week—it was the same idea as the physical challenge that took place in August, just with a lot less IRL dirt.
The awards ceremony and team presentations are in this next video, and we’ll have more on this once we get back from IROS.
[ DARPA SubT ]
NASA is sending a mobile robot to the south pole of the Moon to get a close-up view of the location and concentration of water ice in the region and for the first time ever, actually sample the water ice at the same pole where the first woman and next man will land in 2024 under the Artemis program.
About the size of a golf cart, the Volatiles Investigating Polar Exploration Rover, or VIPER, will roam several miles, using its four science instruments — including a 1-meter drill — to sample various soil environments. Planned for delivery in December 2022, VIPER will collect about 100 days of data that will be used to inform development of the first global water resource maps of the Moon.
[ NASA ]
Happy Halloween from HEBI Robotics!
[ HEBI ]
Happy Halloween from Soft Robotics!
[ Soft Robotics ]
Halloween must be really, really confusing for autonomous cars.
[ Waymo ]
Once a year at Halloween, hardworking JPL engineers put their skills to the test in a highly competitive pumpkin carving contest. The result: A pumpkin gently landed on the Moon, its retrorockets smoldering, while across the room a Nemo-inspired pumpkin explored the sub-surface ocean of Jupiter moon Europa. Suffice to say that when the scientists and engineers at NASA’s Jet Propulsion Laboratory compete in a pumpkin-carving contest, the solar system’s the limit. Take a look at some of the masterpieces from 2019.
Now in its ninth year, the contest gives teams only one hour to carve and decorate their pumpkin though they can prepare non-pumpkin materials – like backgrounds, sound effects and motorized parts – ahead of time.
[ JPL ]
The online autonomous navigation and semantic mapping experiment presented [below] is conducted with the Cassie Blue bipedal robot at the University of Michigan. The sensors attached to the robot include an IMU, a 32-beam LiDAR and an RGB-D camera. The whole online process runs in real-time on a Jetson Xavier and a laptop with an i7 processor.
[ BPL ]
Misty II is now available to anyone who wants one, and she’s on sale for a mere $2900.
[ Misty ]
We leveraged LIDAR-based slam, in conjunction with our specialized relative localization sensor UVDAR to perform a de-centralized, communication-free swarm flight without the units knowing their absolute locations. The swarming and obstacle avoidance control is based on a modified Boids-like algorithm, while the whole swarm is controlled by directing a selected leader unit.
[ MRS ]
The MallARD robot is an autonomous surface vehicle (ASV), designed for the monitoring and inspection of wet storage facilities for example spent fuel pools or wet silos. The MallARD is holonomic, uses a LiDAR for localisation and features a robust trajectory tracking controller.
The University of Manchester’s researcher Dr Keir Groves designed and built the autonomous surface vehicle (ASV) for the challenge which came in the top three of the second round in Nov 2017. The MallARD went on to compete in a final 3rd round where it was deployed in a spent fuel pond at a nuclear power plant in Finland by the IAEA, along with two other entries. The MallARD came second overall, in November 2018.
[ RNE ]
Thanks Jennifer!
I sometimes get the sense that in the robotic grasping and manipulation world, suction cups are kinda seen as cheating at times. But, their nature allows you to do some pretty interesting things.
More clever octopus footage please.
[ CMU ]
A Personal, At-Home Teacher For Playful Learning: From academic topics to child-friendly news bulletins, fun facts and more, Miko 2 is packed with relevant and freshly updated content specially designed by educationists and child-specialists. Your little one won’t even realize they’re learning.
As we point out pretty much every time we post a video like this, keep in mind that you’re seeing a heavily edited version of a hypothetical best case scenario for how this robot can function. And things like “creating a relationship that they can then learn how to form with their peers” is almost certainly overselling things. But at $300 (shipping included), this may be a decent robot as long as your expectations are appropriately calibrated.
[ Miko ]
ICRA 2018 plenary talk by Rodney Brooks: “Robots and People: the Research Challenge.”
[ IEEE RAS ]
ICRA-X 2018 talk by Ron Arkin: “Lethal Autonomous Robots and the Plight of the Noncombatant.”
[ IEEE RAS ]
On the most recent episode of the AI Podcast, Lex Fridman interviews Garry Kasparov.
[ AI Podcast ] Continue reading
Posted in Human Robots