Tag Archives: model

#437763 Peer Review of Scholarly Research Gets ...

Posted on November 28, 2020 by Android

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

#437758 Remotely Operated Robot Takes Straight ...

Posted on November 28, 2020 by Android

Roboticists love hard problems. Challenges like the DRC and SubT have helped (and are still helping) to catalyze major advances in robotics, but not all hard problems require a massive amount of DARPA funding—sometimes, a hard problem can just be something very specific that’s really hard for a robot to do, especially relative to the ease with which a moderately trained human might be able to do it. Catching a ball. Putting a peg in a hole. Or using a straight razor to shave someone’s face without Sweeney Todd-izing them.

This particular roboticist who sees straight-razor face shaving as a hard problem that robots should be solving is John Peter Whitney, who we first met back at IROS 2014 in Chicago when (working at Disney Research) he introduced an elegant fluidic actuator system. These actuators use tubes containing a fluid (like air or water) to transmit forces from a primary robot to a secondary robot in a very efficient way that also allows for either compliance or very high fidelity force feedback, depending on the compressibility of the fluid.

Photo: John Peter Whitney/Northeastern University

Barber meets robot: Boston based barber Jesse Cabbage [top, right] observes the machine created by roboticist John Peter Whitney. Before testing the robot on Whitney’s face, they used his arm for a quick practice [bottom].

Whitney is now at Northeastern University, in Boston, and he recently gave a talk at the RSS workshop on “Reacting to Contact,” where he suggested that straight razor shaving would be an interesting and valuable problem for robotics to work toward, due to its difficulty and requirement for an extremely high level of both performance and reliability.

Now, a straight razor is sort of like a safety razor, except with the safety part removed, which in fact does make it significantly less safe for humans, much less robots. Also not ideal for those worried about safety is that as part of the process the razor ends up in distressingly close proximity to things like the artery that is busily delivering your brain’s entire supply of blood, which is very close to the top of the list of things that most people want to keep blades very far away from. But that didn’t stop Whitney from putting his whiskers where his mouth is and letting his robotic system mediate the ministrations of a professional barber. It’s not an autonomous robotic straight-razor shave (because Whitney is not totally crazy), but it’s a step in that direction, and requires that the hardware Whitney developed be dead reliable.

Perhaps that was a poor choice of words. But, rest assured that Whitney lived long enough to answer our questions after. Here’s the video; it’s part of a longer talk, but it should start in the right spot, at about 23:30.

If Whitney looked a little bit nervous to you, that’s because he was. “This was the first time I’d ever been shaved by someone (something?!) else with a straight razor,” he told us, and while having a professional barber at the helm was some comfort, “the lack of feeling and control on my part was somewhat unsettling.” Whitney says that the barber, Jesse Cabbage of Dentes Barbershop in Somerville, Mass., was surprised by how well he could feel the tactile sensations being transmitted from the razor. “That’s one of the reasons we decided to make this video,” Whitney says. “I can’t show someone how something feels, so the next best thing is to show a delicate task that either from experience or intuition makes it clear to the viewer that the system must have these properties—otherwise the task wouldn’t be possible.”

And as for when Whitney might be comfortable getting shaved by a robotic system without a human in the loop? It’s going to take a lot of work, as do most other hard problems in robotics. “There are two parts to this,” he explains. “One is fault-tolerance of the components themselves (software, electronics, etc.) and the second is the quality of the perception and planning algorithms.”

He offers a comparison to self-driving cars, in which similar (or greater) risks are incurred: “To learn how to perceive, interpret, and adapt, we need a very high-fidelity model of the problem, or a wealth of data and experience, or both” he says. “But in the case of shaving we are greatly lacking in both!” He continues with the analogy: “I think there is a natural progression—the community started with autonomous driving of toy cars on closed courses and worked up to real cars carrying human passengers; in robotic manipulation we are beginning to move out of the ‘toy car’ stage and so I think it’s good to target high-consequence hard problems to help drive progress.”

The ultimate goal is much more general than the creation of a dedicated straight razor shaving robot. This particular hardware system is actually a testbed for exploring MRI-compatible remote needle biopsy.

Of course, the ultimate goal here is much more general than the creation of a dedicated straight razor shaving robot; it’s a challenge that includes a host of sub-goals that will benefit robotics more generally. This particular hardware system Whitney is developing is actually a testbed for exploring MRI-compatible remote needle biopsy, and he and his students are collaborating with Brigham and Women’s Hospital in Boston on adapting this technology to prostate biopsy and ablation procedures. They’re also exploring how delicate touch can be used as a way to map an environment and localize within it, especially where using vision may not be a good option. “These traits and behaviors are especially interesting for applications where we must interact with delicate and uncertain environments,” says Whitney. “Medical robots, assistive and rehabilitation robots and exoskeletons, and shared-autonomy teleoperation for delicate tasks.”
A paper with more details on this robotic system, “Series Elastic Force Control for Soft Robotic Fluid Actuators,” is available on arXiv. Continue reading →

Posted in Human Robots

#437723 Minuscule RoBeetle Turns Liquid Methanol ...

Posted on November 26, 2020 by Android

It’s no secret that one of the most significant constraints on robots is power. Most robots need lots of it, and it has to come from somewhere, with that somewhere usually being a battery because there simply aren’t many other good options. Batteries, however, are famous for having poor energy density, and the smaller your robot is, the more of a problem this becomes. And the issue with batteries goes beyond the battery itself, but also carries over into all the other components that it takes to turn the stored energy into useful work, which again is a particular problem for small-scale robots.

In a paper published this week in Science Robotics, researchers from the University of Southern California, in Los Angeles, demonstrate RoBeetle, an 88-milligram four legged robot that runs entirely on methanol, a power-dense liquid fuel. Without any electronics at all, it uses an exceptionally clever bit of mechanical autonomy to convert methanol vapor directly into forward motion, one millimeter-long step at a time.

It’s not entirely clear from the video how the robot actually works, so let’s go through how it’s put together, and then look at the actuation cycle.

Image: Science Robotics

RoBeetle (A) uses a methanol-based actuation mechanism (B). The robot’s body (C) includes the fuel tank subassembly (D), a tank lid, transmission, and sliding shutter (E), bottom side of the sliding shutter (F), nickel-titanium-platinum composite wire and leaf spring (G), and front legs and hind legs with bioinspired backward-oriented claws (H).

The body of RoBeetle is a boxy fuel tank that you can fill with methanol by poking a syringe through a fuel inlet hole. It’s a quadruped, more or less, with fixed hind legs and two front legs attached to a single transmission that moves them both at once in a sort of rocking forward and up followed by backward and down motion. The transmission is hooked up to a leaf spring that’s tensioned to always pull the legs backward, such that when the robot isn’t being actuated, the spring and transmission keep its front legs more or less vertical and allow the robot to stand. Those horns are primarily there to hold the leaf spring in place, but they’ve got little hooks that can carry stuff, too.

The actuator itself is a nickel-titanium (NiTi) shape-memory alloy (SMA), which is just a wire that gets longer when it heats up and then shrinks back down when it cools. SMAs are fairly common and used for all kinds of things, but what makes this particular SMA a little different is that it’s been messily coated with platinum. The “messily” part is important for a reason that we’ll get to in just a second.

The way that the sliding vent is attached to the transmission is the really clever bit about this robot, because it means that the motion of the wire itself is used to modulate the flow of fuel through a purely mechanical system. Essentially, it’s an actuator and a sensor at the same time.

One end of the SMA wire is attached to the middle of the leaf spring, while the other end runs above the back of the robot where it’s stapled to an anchor block on the robot’s rear end. With the SMA wire hooked up but not actuated (i.e., cold rather than warm), it’s short enough that the leaf spring gets pulled back, rocking the legs forward and up. The last component is embedded in the robot’s back, right along the spine and directly underneath the SMA actuator. It’s a sliding vent attached to the transmission, so that the vent is open when the SMA wire is cold and the leaf spring is pulled back, and closed when the SMA wire is warm and the leaf spring is relaxed. The way that the sliding vent is attached to the transmission is the really clever bit about this robot, because it means that the motion of the wire itself is used to modulate the flow of fuel through a purely mechanical system. Essentially, it’s an actuator and a sensor at the same time.

The actuation cycle that causes the robot to walk begins with a full fuel tank and a cold SMA wire. There’s tension on the leaf spring, pulling the transmission back and rocking the legs forward and upward. The transmission also pulls the sliding vent into the open position, allowing methanol vapor to escape up out of the fuel tank and into the air, where it wafts past the SMA wire that runs directly above the vent.

The platinum facilitates a reaction of the methanol (CH3OH) with oxygen in the air (combustion, although not the dramatic flaming and explosive kind) to generate a couple of water molecules and some carbon dioxide plus a bunch of heat, and this is where the messy platinum coating is important, because messy means lots of surface area for the platinum to interact with as much methanol as possible. In just a second or two the temperature of the SMA wire skyrockets from 50 to 100 ºC and it expands, allowing the leaf spring about 0.1 mm of slack. As the leaf spring relaxes, the transmission moves the legs backwards and downwards, and the robot pulls itself forward about 1.2 mm. At the same time, the transmission is closing off the sliding vent, cutting off the supply of methanol vapor. Without the vapor reacting with the platinum and generating heat, in about a second and a half, the SMA wire cools down. As it does, it shrinks, pulling on the leaf spring and starting the cycle over again. Top speed is 0.76 mm/s (0.05 body-lengths per second).

An interesting environmental effect is that the speed of the robot can be enhanced by a gentle breeze. This is because air moving over the SMA wire cools it down a bit faster while also blowing away any residual methanol from around the vents, shutting down the reaction more completely. RoBeetle can carry more than its own body weight in fuel, and it takes approximately 155 minutes for a full tank of methanol to completely evaporate. It’s worth noting that despite the very high energy density of methanol, this is actually a stupendously inefficient way of powering a robot, with an estimated end-to-end efficiency of just 0.48 percent. Not 48 percent, mind you, but 0.48 percent, while in general, powering SMAs with electricity is much more efficient.

However, you have to look at the entire system that would be necessary to deliver that electricity, and for a robot as small as RoBeetle, the researchers say that it’s basically impossible. The lightest commercially available battery and power supply that would deliver enough juice to heat up an SMA actuator weighs about 800 mg, nearly 10 times the total weight of RoBeetle itself. From that perspective, RoBeetle’s efficiency is actually pretty good.

Image: A. Kitterman/Science Robotics; adapted from R.L.T./MIT

Comparison of various untethered microrobots and bioinspired soft robots that use different power and actuation strategies.

There are some other downsides to RoBeetle we should mention—it can only move forwards, not backwards, and it can’t steer. Its speed isn’t adjustable, and once it starts walking, it’ll walk until it either breaks or runs out of fuel. The researchers have some ideas about the speed, at least, pointing out that increasing the speed of fuel delivery by using pressurized liquid fuels like butane or propane would increase the actuator output frequency. And the frequency, amplitude, and efficiency of the SMAs themselves can be massively increased “by arranging multiple fiber-like thin artificial muscles in hierarchical configurations similar to those observed in sarcomere-based animal muscle,” making RoBeetle even more beetle-like.

As for sensing, RoBeetle’s 230-mg payload is enough to carry passive sensors, but getting those sensors to usefully interact with the robot itself to enable any kind of autonomy remains a challenge. Mechanically intelligence is certainly possible, though, and we can imagine RoBeetle adopting some of the same sorts of systems that have been proposed for the clockwork rover that JPL wants to use for Venus exploration. The researchers also mention how RoBeetle could potentially serve as a model for microbots capable of aerial locomotion, which is something we’d very much like to see.

“An 88-milligram insect-scale autonomous crawling robot driven by a catalytic artificial muscle,” by Xiufeng Yang, Longlong Chang, and Néstor O. Pérez-Arancibia from University of Southern California, in Los Angeles, was published in Science Robotics. Continue reading →

Posted in Human Robots

#437709 iRobot Announces Major Software Update, ...

Posted on November 25, 2020 by Android

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

#437707 Video Friday: This Robot Will Restock ...

Posted on November 25, 2020 by Android

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]
ICUAS 2020 – September 1-4, 2020 – Athens, Greece
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.

Tokyo startup Telexistence has recently unveiled a new robot called the Model-T, an advanced teleoperated humanoid that can use tools and grasp a wide range of objects. Japanese convenience store chain FamilyMart plans to test the Model-T to restock shelves in up to 20 stores by 2022. In the trial, a human “pilot” will operate the robot remotely, handling items like beverage bottles, rice balls, sandwiches, and bento boxes.

With Model-T and AWP, FamilyMart and TX aim to realize a completely new store operation by remoteizing and automating the merchandise restocking work, which requires a large number of labor-hours. As a result, stores can operate with less number of workers and enable them to recruit employees regardless of the store’s physical location.

[ Telexistence ]

Quadruped dance-off should be a new robotics competition at IROS or ICRA.

I dunno though, that moonwalk might keep Spot in the lead…

[ Unitree ]

Through a hybrid of simulation and real-life training, this air muscle robot is learning to play table tennis.

Table tennis requires to execute fast and precise motions. To gain precision it is necessary to explore in this high-speed regimes, however, exploration can be safety-critical at the same time. The combination of RL and muscular soft robots allows to close this gap. While robots actuated by pneumatic artificial muscles generate high forces that are required for e.g. smashing, they also offer safe execution of explosive motions due to antagonistic actuation.

To enable practical training without real balls, we introduce Hybrid Sim and Real Training (HYSR) that replays prerecorded real balls in simulation while executing actions on the real system. In this manner, RL can learn the challenging motor control of the PAM-driven robot while executing ~15000 hitting motions.

[ Max Planck Institute ]

Thanks Dieter!

Anthony Cowley wrote in to share his recent thesis work on UPSLAM, a fast and lightweight SLAM technique that records data in panoramic depth images (just PNGs) that are easy to visualize and even easier to share between robots, even on low-bandwidth networks.

[ UPenn ]

Thanks Anthony!

GITAI’s G1 is the space dedicated general-purpose robot. G1 robot will enable automation of various tasks internally & externally on space stations and for lunar base development.

[ Gitai ]

The University of Michigan has a fancy new treadmill that’s built right into the floor, which proves to be a bit much for Mini Cheetah.

But Cassie Blue won’t get stuck on no treadmill! She goes for a 0.3 mile walk across campus, which ends when a certain someone ran the gantry into Cassie Blue’s foot.

[ Michigan Robotics ]

Some serious quadruped research going on at UT Austin Human Centered Robotics Lab.

[ HCRL ]

Will Burrard-Lucas has spent lockdown upgrading his slightly indestructible BeetleCam wildlife photographing robot.

[ Will Burrard-Lucas ]

Teleoperated surgical robots are becoming commonplace in operating rooms, but many are massive (sometimes taking up an entire room) and are difficult to manipulate, especially if a complication arises and the robot needs to removed from the patient. A new collaboration between the Wyss Institute, Harvard University, and Sony Corporation has created the mini-RCM, a surgical robot the size of a tennis ball that weighs as much as a penny, and performed significantly better than manually operated tools in delicate mock-surgical procedures. Importantly, its small size means it is more comparable to the human tissues and structures on which it operates, and it can easily be removed by hand if needed.

[ Harvard Wyss ]

Yaskawa appears to be working on a robot that can scan you with a temperature gun and then jam a mask on your face?

[ Motoman ]

Maybe we should just not have people working in mines anymore, how about that?

[ Exyn ]

Many current human-robot interactive systems tend to use accurate and fast – but also costly – actuators and tracking systems to establish working prototypes that are safe to use and deploy for user studies. This paper presents an embedded framework to build a desktop space for human-robot interaction, using an open-source robot arm, as well as two RGB cameras connected to a Raspberry Pi-based controller that allow a fast yet low-cost object tracking and manipulation in 3D. We show in our evaluations that this facilitates prototyping a number of systems in which user and robot arm can commonly interact with physical objects.

[ Paper ]

IBM Research is proud to host professor Yoshua Bengio — one of the world’s leading experts in AI — in a discussion of how AI can contribute to the fight against COVID-19.

[ IBM Research ]

Ira Pastor, ideaXme life sciences ambassador interviews Professor Dr. Hiroshi Ishiguro, the Director of the Intelligent Robotics Laboratory, of the Department of Systems Innovation, in the Graduate School of Engineering Science, at Osaka University, Japan.

[ ideaXme ]

A CVPR talk from Stanford’s Chelsea Finn on “Generalization in Visuomotor Learning.”

[ Stanford ] Continue reading →

Posted in Human Robots