Tag Archives: large
#437763 Peer Review of Scholarly Research Gets ...
In the world of academics, peer review is considered the only credible validation of scholarly work. Although the process has its detractors, evaluation of academic research by a cohort of contemporaries has endured for over 350 years, with “relatively minor changes.” However, peer review may be set to undergo its biggest revolution ever—the integration of artificial intelligence.
Open-access publisher Frontiers has debuted an AI tool called the Artificial Intelligence Review Assistant (AIRA), which purports to eliminate much of the grunt work associated with peer review. Since the beginning of June 2020, every one of the 11,000-plus submissions Frontiers received has been run through AIRA, which is integrated into its collaborative peer-review platform. This also makes it accessible to external users, accounting for some 100,000 editors, authors, and reviewers. Altogether, this helps “maximize the efficiency of the publishing process and make peer-review more objective,” says Kamila Markram, founder and CEO of Frontiers.
AIRA’s interactive online platform, which is a first of its kind in the industry, has been in development for three years.. It performs three broad functions, explains Daniel Petrariu, director of project management: assessing the quality of the manuscript, assessing quality of peer review, and recommending editors and reviewers. At the initial validation stage, the AI can make up to 20 recommendations and flag potential issues, including language quality, plagiarism, integrity of images, conflicts of interest, and so on. “This happens almost instantly and with [high] accuracy, far beyond the rate at which a human could be expected to complete a similar task,” Markram says.
“We have used a wide variety of machine-learning models for a diverse set of applications, including computer vision, natural language processing, and recommender systems,” says Markram. This includes simple bag-of-words models, as well as more sophisticated deep-learning ones. AIRA also leverages a large knowledge base of publications and authors.
Markram notes that, to address issues of possible AI bias, “We…[build] our own datasets and [design] our own algorithms. We make sure no statistical biases appear in the sampling of training and testing data. For example, when building a model to assess language quality, scientific fields are equally represented so the model isn’t biased toward any specific topic.” Machine- and deep-learning approaches, along with feedback from domain experts, including errors, are captured and used as additional training data. “By regularly re-training, we make sure our models improve in terms of accuracy and stay up-to-date.”
The AI’s job is to flag concerns; humans take the final decisions, says Petrariu. As an example, he cites image manipulation detection—something AI is super-efficient at but is nearly impossible for a human to perform with the same accuracy. “About 10 percent of our flagged images have some sort of problem,” he adds. “[In academic publishing] nobody has done this kind of comprehensive check [using AI] before,” says Petrariu. AIRA, he adds, facilitates Frontiers’ mission to make science open and knowledge accessible to all. Continue reading
Posted in Human Robots
#437709 iRobot Announces Major Software Update, ...
Since the release of the very first Roomba in 2002, iRobot’s long-term goal has been to deliver cleaner floors in a way that’s effortless and invisible. Which sounds pretty great, right? And arguably, iRobot has managed to do exactly this, with its most recent generation of robot vacuums that make their own maps and empty their own dustbins. For those of us who trust our robots, this is awesome, but iRobot has gradually been realizing that many Roomba users either don’t want this level of autonomy, or aren’t ready for it.
Today, iRobot is announcing a major new update to its app that represents a significant shift of its overall approach to home robot autonomy. Humans are being brought back into the loop through software that tries to learn when, where, and how you clean so that your Roomba can adapt itself to your life rather than the other way around.
To understand why this is such a shift for iRobot, let’s take a very brief look back at how the Roomba interface has evolved over the last couple of decades. The first generation of Roomba had three buttons on it that allowed (or required) the user to select whether the room being vacuumed was small or medium or large in size. iRobot ditched that system one generation later, replacing the room size buttons with one single “clean” button. Programmable scheduling meant that users no longer needed to push any buttons at all, and with Roombas able to find their way back to their docking stations, all you needed to do was empty the dustbin. And with the most recent few generations (the S and i series), the dustbin emptying is also done for you, reducing direct interaction with the robot to once a month or less.
Image: iRobot
iRobot CEO Colin Angle believes that working toward more intelligent human-robot collaboration is “the brave new frontier” of AI. “This whole journey has been earning the right to take this next step, because a robot can’t be responsive if it’s incompetent,” he says. “But thinking that autonomy was the destination was where I was just completely wrong.”
The point that the top-end Roombas are at now reflects a goal that iRobot has been working toward since 2002: With autonomy, scheduling, and the clean base to empty the bin, you can set up your Roomba to vacuum when you’re not home, giving you cleaner floors every single day without you even being aware that the Roomba is hard at work while you’re out. It’s not just hands-off, it’s brain-off. No noise, no fuss, just things being cleaner thanks to the efforts of a robot that does its best to be invisible to you. Personally, I’ve been completely sold on this idea for home robots, and iRobot CEO Colin Angle was as well.
“I probably told you that the perfect Roomba is the Roomba that you never see, you never touch, you just come home everyday and it’s done the right thing,” Angle told us. “But customers don’t want that—they want to be able to control what the robot does. We started to hear this a couple years ago, and it took a while before it sunk in, but it made sense.”
How? Angle compares it to having a human come into your house to clean, but you weren’t allowed to tell them where or when to do their job. Maybe after a while, you’ll build up the amount of trust necessary for that to work, but in the short term, it would likely be frustrating. And people get frustrated with their Roombas for this reason. “The desire to have more control over what the robot does kept coming up, and for me, it required a pretty big shift in my view of what intelligence we were trying to build. Autonomy is not intelligence. We need to do something more.”
That something more, Angle says, is a partnership as opposed to autonomy. It’s an acknowledgement that not everyone has the same level of trust in robots as the people who build them. It’s an understanding that people want to have a feeling of control over their homes, that they have set up the way that they want, and that they’ve been cleaning the way that they want, and a robot shouldn’t just come in and do its own thing.
This change in direction also represents a substantial shift in resources for iRobot, and the company has pivoted two-thirds of its engineering organization to focus on software-based collaborative intelligence rather than hardware.
“Until the robot proves that it knows enough about your home and about the way that you want your home cleaned,” Angle says, “you can’t move forward.” He adds that this is one of those things that seem obvious in retrospect, but even if they’d wanted to address the issue before, they didn’t have the technology to solve the problem. Now they do. “This whole journey has been earning the right to take this next step, because a robot can’t be responsive if it’s incompetent,” Angle says. “But thinking that autonomy was the destination was where I was just completely wrong.”
The previous iteration of the iRobot app (and Roombas themselves) are built around one big fat CLEAN button. The new approach instead tries to figure out in much more detail where the robot should clean, and when, using a mixture of autonomous technology and interaction with the user.
Where to Clean
Knowing where to clean depends on your Roomba having a detailed and accurate map of its environment. For several generations now, Roombas have been using visual mapping and localization (VSLAM) to build persistent maps of your home. These maps have been used to tell the Roomba to clean in specific rooms, but that’s about it. With the new update, Roombas with cameras will be able to recognize some objects and features in your home, including chairs, tables, couches, and even countertops. The robots will use these features to identify where messes tend to happen so that they can focus on those areas—like around the dining room table or along the front of the couch.
We should take a minute here to clarify how the Roomba is using its camera. The original (primary?) purpose of the camera was for VSLAM, where the robot would take photos of your home, downsample them into QR-code-like patterns of light and dark, and then use those (with the assistance of other sensors) to navigate. Now the camera is also being used to take pictures of other stuff around your house to make that map more useful.
Photo: iRobot
The robots will now try to fit into the kinds of cleaning routines that many people already have established. For example, the app may suggest an “after dinner” routine that cleans just around the kitchen and dining room table.
This is done through machine learning using a library of images of common household objects from a floor perspective that iRobot had to develop from scratch. Angle clarified for us that this is all done via a neural net that runs on the robot, and that “no recognizable images are ever stored on the robot or kept, and no images ever leave the robot.” Worst case, if all the data iRobot has about your home gets somehow stolen, the hacker would only know that (for example) your dining room has a table in it and the approximate size and location of that table, because the map iRobot has of your place only stores symbolic representations rather than images.
Another useful new feature is intended to help manage the “evil Roomba places” (as Angle puts it) that every home has that cause Roombas to get stuck. If the place is evil enough that Roomba has to call you for help because it gave up completely, Roomba will now remember, and suggest that either you make some changes or that it stops cleaning there, which seems reasonable.
When to Clean
It turns out that the primary cause of mission failure for Roombas is not that they get stuck or that they run out of battery—it’s user cancellation, usually because the robot is getting in the way or being noisy when you don’t want it to be. “If you kill a Roomba’s job because it annoys you,” points out Angle, “how is that robot being a good partner? I think it’s an epic fail.” Of course, it’s not the robot’s fault, because Roombas only clean when we tell them to, which Angle says is part of the problem. “People actually aren’t very good at making their own schedules—they tend to oversimplify, and not think through what their schedules are actually about, which leads to lots of [figurative] Roomba death.”
To help you figure out when the robot should actually be cleaning, the new app will look for patterns in when you ask the robot to clean, and then recommend a schedule based on those patterns. That might mean the robot cleans different areas at different times every day of the week. The app will also make scheduling recommendations that are event-based as well, integrated with other smart home devices. Would you prefer the Roomba to clean every time you leave the house? The app can integrate with your security system (or garage door, or any number of other things) and take care of that for you.
More generally, Roomba will now try to fit into the kinds of cleaning routines that many people already have established. For example, the app may suggest an “after dinner” routine that cleans just around the kitchen and dining room table. The app will also, to some extent, pay attention to the environment and season. It might suggest increasing your vacuuming frequency if pollen counts are especially high, or if it’s pet shedding season and you have a dog. Unfortunately, Roomba isn’t (yet?) capable of recognizing dogs on its own, so the app has to cheat a little bit by asking you some basic questions.
A Smarter App
Image: iRobot
The previous iteration of the iRobot app (and Roombas themselves) are built around one big fat CLEAN button. The new approach instead tries to figure out in much more detail where the robot should clean, and when, using a mixture of autonomous technology and interaction with the user.
The app update, which should be available starting today, is free. The scheduling and recommendations will work on every Roomba model, although for object recognition and anything related to mapping, you’ll need one of the more recent and fancier models with a camera. Future app updates will happen on a more aggressive schedule. Major app releases should happen every six months, with incremental updates happening even more frequently than that.
Angle also told us that overall, this change in direction also represents a substantial shift in resources for iRobot, and the company has pivoted two-thirds of its engineering organization to focus on software-based collaborative intelligence rather than hardware. “It’s not like we’re done doing hardware,” Angle assured us. “But we do think about hardware differently. We view our robots as platforms that have longer life cycles, and each platform will be able to support multiple generations of software. We’ve kind of decoupled robot intelligence from hardware, and that’s a change.”
Angle believes that working toward more intelligent collaboration between humans and robots is “the brave new frontier of artificial intelligence. I expect it to be the frontier for a reasonable amount of time to come,” he adds. “We have a lot of work to do to create the type of easy-to-use experience that consumer robots need.” Continue reading
Posted in Human Robots
#437695 Video Friday: Even Robots Know That You ...
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!):
CLAWAR 2020 – August 24-26, 2020 – [Online Conference]
Other Than Human – September 3-10, 2020 – Stockholm, Sweden
ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online Conference]
IROS 2020 – October 25-29, 2020 – Las Vegas, Nev., USA
CYBATHLON 2020 – November 13-14, 2020 – [Online Event]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today's videos.
From the Robotics and Perception Group at UZH comes Flightmare, a simulation environment for drones that combines a slick rendering engine with a robust physics engine that can run as fast as your system can handle.
Flightmare is composed of two main components: a configurable rendering engine built on Unity and a flexible physics engine for dynamics simulation. Those two components are totally decoupled and can run independently from each other. Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene; (ii) an API for reinforcement learning which can simulate hundreds of quadrotors in parallel; and (iii) an integration with a virtual-reality headset for interaction with the simulated environment. Flightmare can be used for various applications, including path-planning, reinforcement learning, visual-inertial odometry, deep learning, human-robot interaction, etc.
[ Flightmare ]
Quadruped robots yelling at people to maintain social distancing is really starting to become a thing, for better or worse.
We introduce a fully autonomous surveillance robot based on a quadruped platform that can promote social distancing in complex urban environments. Specifically, to achieve autonomy, we mount multiple cameras and a 3D LiDAR on the legged robot. The robot then uses an onboard real-time social distancing detection system to track nearby pedestrian groups. Next, the robot uses a crowd-aware navigation algorithm to move freely in highly dynamic scenarios. The robot finally uses a crowd aware routing algorithm to effectively promote social distancing by using human-friendly verbal cues to send suggestions to overcrowded pedestrians.
[ Project ]
Thanks Fan!
The Personal Robotics Group at Oregon State University is looking at UV germicidal irradiation for surface disinfection with a Fetch Manipulator Robot.
Fetch Robot disinfecting dance party woo!
[ Oregon State ]
How could you not take a mask from this robot?
[ Reachy ]
This work presents the design, development and autonomous navigation of the alpha-version of our Resilient Micro Flyer, a new type of collision-tolerant small aerial robot tailored to traversing and searching within highly confined environments including manhole-sized tubes. The robot is particularly lightweight and agile, while it implements a rigid collision-tolerant design which renders it resilient during forcible interaction with the environment. Furthermore, the design of the system is enhanced through passive flaps ensuring smoother and more compliant collision which was identified to be especially useful in very confined settings.
[ ARL ]
Pepper can make maps and autonomously navigate, which is interesting, but not as interesting as its posture when it's wandering around.
Dat backing into the charging dock tho.
[ Pepper ]
RatChair a strategy for displacing big objects by attaching relatively small vibration sources. After learning how several random bursts of vibration affect its pose, an optimization algorithm discovers the optimal sequence of vibration patterns required to (slowly but surely) move the object to a specified position.
This is from 2015, why isn't all of my furniture autonomous yet?!
[ KAIST ]
The new SeaDrone Pro is designed to be the underwater equivalent of a quadrotor. This video is a rendering, but we've been assured that it does actually exist.
[ SeaDrone ]
Thanks Eduardo!
Porous Loops is a lightweight composite facade panel that shows the potential of 3D printing of mineral foams for building scale applications.
[ ETH ]
Thanks Fan!
Here's an interesting idea for a robotic gripper- it's what appears to be a snap bracelet coupled to a pneumatic actuator that allows the snap bracelet to be reset.
[ Georgia Tech ]
Graze is developing a commercial robotic lawnmower. They're also doing a sort of crowdfunded investment thing, which probably explains the painfully overproduced nature of the following video:
A couple things about this: the hard part, which the video skips over almost entirely, is the mapping, localization, and understanding where to mow and where not to mow. The pitch deck seems to suggest that this is mostly done through computer vision, a thing that's perhaps easy to do under controlled ideal conditions, but difficult to apply to a world full lawns that are all different. The commercial aspect is interesting because golf courses are likely as standardized as you can get, but the emphasis here on how much money they can make without really addressing any of the technical stuff makes me raise an eyebrow or two.
[ Graze ]
The record & playback X-series arm demo allows the user to record the arm's movements while motors are torqued off. Then, the user may torque the motor's on and watch the movements they just made playback!
[ Interbotix ]
Shadow Robot has a new teleop system for its hand. I'm guessing that it's even trickier to use than it looks.
[ Shadow Robot ]
Quanser Interactive Labs is a collection of virtual hardware-based laboratory activities that supplement traditional or online courses. Same as working with physical systems in the lab, students work with virtual twins of Quanser's most popular plants, develop their mathematical models, implement and simulate the dynamic behavior of these systems, design controllers, and validate them on a high-fidelity 3D real-time virtual models. The virtual systems not only look like the real ones, they also behave, can be manipulated, measured, and controlled like real devices. And finally, when students go to the lab, they can deploy their virtually-validated designs on actual physical equipment.
[ Quanser ]
This video shows robot-assisted heart surgery. It's amazing to watch if you haven't seen this sort of thing before, but be aware that there is a lot of blood.
This video demonstrates a fascinating case of robotic left atrial myxoma excision, narrated by Joel Dunning, Middlesbrough, UK. The Robotic platform provides superior visualisation and enhanced dexterity, through keyhole incisions. Robotic surgery is an integral part of our Minimally Invasive Cardiothoracic Surgery Program.
[ Tristan D. Yan ]
Thanks Fan!
In this talk, we present our work on learning control policies directly in simulation that are deployed onto real drones without any fine tuning. The presentation covers autonomous drone racing, drone acrobatics, and uncertainty estimation in deep networks.
[ RPG ] Continue reading
Posted in Human Robots