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#437687 Video Friday: Bittle Is a Palm-Sized ...
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!):
ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online]
IROS 2020 – October 25-29, 2020 – [Online]
CYBATHLON 2020 – November 13-14, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today's videos.
Rongzhong Li, who is responsible for the adorable robotic cat Nybble, has an updated and even more adorable quadruped that's more robust and agile but only costs around US $200 in kit form on Kickstarter.
Looks like the early bird options are sold out, but a full kit is a $225 pledge, for delivery in December.
[ Kickstarter ]
Thanks Rz!
I still maintain that Stickybot was one of the most elegantly designed robots ever.
[ Stanford ]
With the unpredictable health crisis of COVID-19 continuing to place high demands on hospitals, PAL Robotics have successfully completed testing of their delivery robots in Barcelona hospitals this summer. The TIAGo Delivery and TIAGo Conveyor robots were deployed in Hospital Municipal of Badalona and Hospital Clínic Barcelona following a winning proposal submitted to the European DIH-Hero project. Accerion sensors were integrated onto the TIAGo Delivery Robot and TIAGo Conveyor Robot for use in this project.
[ PAL Robotics ]
Energy Robotics, a leading developer of software solutions for mobile robots used in industrial applications, announced that its remote sensing and inspection solution for Boston Dynamics’s agile mobile robot Spot was successfully deployed at Merck’s thermal exhaust treatment plant at its headquarters in Darmstadt, Germany. Energy Robotics equipped Spot with sensor technology and remote supervision functions to support the inspection mission.
Combining Boston Dynamics’ intuitive controls, robotic intelligence and open interface with Energy Robotics’ control and autonomy software, user interface and encrypted cloud connection, Spot can be taught to autonomously perform a specific inspection round while being supervised remotely from anywhere with internet connectivity. Multiple cameras and industrial sensors enable the robot to find its way around while recording and transmitting information about the facility’s onsite equipment operations.
Spot reads the displays of gauges in its immediate vicinity and can also zoom in on distant objects using an externally-mounted optical zoom lens. In the thermal exhaust treatment facility, for instance, it monitors cooling water levels and notes whether condensation water has accumulated. Outside the facility, Spot monitors pipe bridges for anomalies.
Among the robot’s many abilities, it can detect defects of wires or the temperature of pump components using thermal imaging. The robot was put through its paces on a comprehensive course that tested its ability to handle special challenges such as climbing stairs, scaling embankments and walking over grating.
[ Energy Robotics ]
Thanks Stefan!
Boston Dynamics really should give Dr. Guero an Atlas just to see what he can do with it.
[ DrGuero ]
World's First Socially Distanced Birthday Party: Located in London, the robotic arm was piloted in real time to light the candles on the cake by the founder of Extend Robotics, Chang Liu, who was sat 50 miles away in Reading. Other team members in Manchester and Reading were also able to join in the celebration as the robot was used to accurately light the candles on the birthday cake.
[ Extend Robotics ]
The Robocon in-person competition was canceled this year, but check out Tokyo University's robots in action:
[ Robocon ]
Sphero has managed to pack an entire Sphero into a much smaller sphere.
[ Sphero ]
Squishy Robotics, a small business funded by the National Science Foundation (NSF), is developing mobile sensor robots for use in disaster rescue, remote monitoring, and space exploration. The shape-shifting, mobile, senor robots from UC-Berkeley spin-off Squishy Robotics can be dropped from airplanes or drones and can provide first responders with ground-based situational awareness during fires, hazardous materials (HazMat) release, and natural and man-made disasters.
[ Squishy Robotics ]
Meet Jasper, the small girl with big dreams to FLY. Created by UTS Animal Logic Academy in partnership with the Royal Australian Air Force to encourage girls to soar above the clouds. Jasper was created using a hybrid of traditional animation techniques and technology such as robotics and 3D printing. A KUKA QUANTEC robot is used during the film making to help the Australian Royal Airforce tell their story in a unique way. UTS adapted their High Accurate robot to film consistent paths, creating a video with physical sets and digital characters.
[ AU AF ]
Impressive what the Ghost Robotics V60 can do without any vision sensors on it.
[ Ghost Robotics ]
Is your job moving tiny amounts of liquid around? Would you rather be doing something else? ABB’s YuMi got you.
[ Yumi ]
For his PhD work at the Media Lab, Biomechatronics researcher Roman Stolyarov developed a terrain-adaptive control system for robotic leg prostheses. as a way to help people with amputations feel as able-bodied and mobile as possible, by allowing them to walk seamlessly regardless of the ground terrain.
[ MIT ]
This robot collects data on each cow when she enters to be milked. Milk samples and 3D photos can be taken to monitor the cow’s health status. The Ontario Dairy Research Centre in Elora, Ontario, is leading dairy innovation through education and collaboration. It is a state-of-the-art 175,000 square foot facility for discovery, learning and outreach. This centre is a partnership between the Agricultural Research Institute of Ontario, OMAFRA, the University of Guelph and the Ontario dairy industry.
[ University of Guleph ]
Australia has one of these now, should the rest of us panic?
[ Boeing ]
Daimler and Torc are developing Level 4 automated trucks for the real world. Here is a glimpse into our closed-course testing, routes on public highways in Virginia, and self-driving capabilities development. Our year of collaborating on the future of transportation culminated in the announcement of our new truck testing center in New Mexico.
[ Torc Robotics ] Continue reading
Posted in Human Robots
#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
#430761 How Robots Are Getting Better at Making ...
The multiverse of science fiction is populated by robots that are indistinguishable from humans. They are usually smarter, faster, and stronger than us. They seem capable of doing any job imaginable, from piloting a starship and battling alien invaders to taking out the trash and cooking a gourmet meal.
The reality, of course, is far from fantasy. Aside from industrial settings, robots have yet to meet The Jetsons. The robots the public are exposed to seem little more than over-sized plastic toys, pre-programmed to perform a set of tasks without the ability to interact meaningfully with their environment or their creators.
To paraphrase PayPal co-founder and tech entrepreneur Peter Thiel, we wanted cool robots, instead we got 140 characters and Flippy the burger bot. But scientists are making progress to empower robots with the ability to see and respond to their surroundings just like humans.
Some of the latest developments in that arena were presented this month at the annual Robotics: Science and Systems Conference in Cambridge, Massachusetts. The papers drilled down into topics that ranged from how to make robots more conversational and help them understand language ambiguities to helping them see and navigate through complex spaces.
Improved Vision
Ben Burchfiel, a graduate student at Duke University, and his thesis advisor George Konidaris, an assistant professor of computer science at Brown University, developed an algorithm to enable machines to see the world more like humans.
In the paper, Burchfiel and Konidaris demonstrate how they can teach robots to identify and possibly manipulate three-dimensional objects even when they might be obscured or sitting in unfamiliar positions, such as a teapot that has been tipped over.
The researchers trained their algorithm by feeding it 3D scans of about 4,000 common household items such as beds, chairs, tables, and even toilets. They then tested its ability to identify about 900 new 3D objects just from a bird’s eye view. The algorithm made the right guess 75 percent of the time versus a success rate of about 50 percent for other computer vision techniques.
In an email interview with Singularity Hub, Burchfiel notes his research is not the first to train machines on 3D object classification. How their approach differs is that they confine the space in which the robot learns to classify the objects.
“Imagine the space of all possible objects,” Burchfiel explains. “That is to say, imagine you had tiny Legos, and I told you [that] you could stick them together any way you wanted, just build me an object. You have a huge number of objects you could make!”
The infinite possibilities could result in an object no human or machine might recognize.
To address that problem, the researchers had their algorithm find a more restricted space that would host the objects it wants to classify. “By working in this restricted space—mathematically we call it a subspace—we greatly simplify our task of classification. It is the finding of this space that sets us apart from previous approaches.”
Following Directions
Meanwhile, a pair of undergraduate students at Brown University figured out a way to teach robots to understand directions better, even at varying degrees of abstraction.
The research, led by Dilip Arumugam and Siddharth Karamcheti, addressed how to train a robot to understand nuances of natural language and then follow instructions correctly and efficiently.
“The problem is that commands can have different levels of abstraction, and that can cause a robot to plan its actions inefficiently or fail to complete the task at all,” says Arumugam in a press release.
In this project, the young researchers crowdsourced instructions for moving a virtual robot through an online domain. The space consisted of several rooms and a chair, which the robot was told to manipulate from one place to another. The volunteers gave various commands to the robot, ranging from general (“take the chair to the blue room”) to step-by-step instructions.
The researchers then used the database of spoken instructions to teach their system to understand the kinds of words used in different levels of language. The machine learned to not only follow instructions but to recognize the level of abstraction. That was key to kickstart its problem-solving abilities to tackle the job in the most appropriate way.
The research eventually moved from virtual pixels to a real place, using a Roomba-like robot that was able to respond to instructions within one second 90 percent of the time. Conversely, when unable to identify the specificity of the task, it took the robot 20 or more seconds to plan a task about 50 percent of the time.
One application of this new machine-learning technique referenced in the paper is a robot worker in a warehouse setting, but there are many fields that could benefit from a more versatile machine capable of moving seamlessly between small-scale operations and generalized tasks.
“Other areas that could possibly benefit from such a system include things from autonomous vehicles… to assistive robotics, all the way to medical robotics,” says Karamcheti, responding to a question by email from Singularity Hub.
More to Come
These achievements are yet another step toward creating robots that see, listen, and act more like humans. But don’t expect Disney to build a real-life Westworld next to Toon Town anytime soon.
“I think we’re a long way off from human-level communication,” Karamcheti says. “There are so many problems preventing our learning models from getting to that point, from seemingly simple questions like how to deal with words never seen before, to harder, more complicated questions like how to resolve the ambiguities inherent in language, including idiomatic or metaphorical speech.”
Even relatively verbose chatbots can run out of things to say, Karamcheti notes, as the conversation becomes more complex.
The same goes for human vision, according to Burchfiel.
While deep learning techniques have dramatically improved pattern matching—Google can find just about any picture of a cat—there’s more to human eyesight than, well, meets the eye.
“There are two big areas where I think perception has a long way to go: inductive bias and formal reasoning,” Burchfiel says.
The former is essentially all of the contextual knowledge people use to help them reason, he explains. Burchfiel uses the example of a puddle in the street. People are conditioned or biased to assume it’s a puddle of water rather than a patch of glass, for instance.
“This sort of bias is why we see faces in clouds; we have strong inductive bias helping us identify faces,” he says. “While it sounds simple at first, it powers much of what we do. Humans have a very intuitive understanding of what they expect to see, [and] it makes perception much easier.”
Formal reasoning is equally important. A machine can use deep learning, in Burchfiel’s example, to figure out the direction any river flows once it understands that water runs downhill. But it’s not yet capable of applying the sort of human reasoning that would allow us to transfer that knowledge to an alien setting, such as figuring out how water moves through a plumbing system on Mars.
“Much work was done in decades past on this sort of formal reasoning… but we have yet to figure out how to merge it with standard machine-learning methods to create a seamless system that is useful in the actual physical world.”
Robots still have a lot to learn about being human, which should make us feel good that we’re still by far the most complex machines on the planet.
Image Credit: Alex Knight via Unsplash Continue reading
Posted in Human Robots