Tag Archives: years

#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

#437745 Video Friday: Japan’s Giant Gundam ...

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!):

AWS Cloud Robotics Summit – August 18-19, 2020 – [Online Conference]
CLAWAR 2020 – August 24-26, 2020 – [Virtual 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
ICSR 2020 – November 14-16, 2020 – Golden, Co., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

It’s coming together—literally! Japan’s giant Gundam appears nearly finished and ready for its first steps. In a recent video, Gundam Factory Yokohama, which is constructing the 18-meter-tall, 25-ton walking robot, provided an update on the project. The video shows the Gundam getting its head attached—after being blessed by Shinto priests.

In the video update, they say the project is “steadily progressing” and further details will be announced around the end of September.

[ Gundam Factory Yokohama ]

Creating robots with emotional personalities will transform the usability of robots in the real-world. As previous emotive social robots are mostly based on statically stable robots whose mobility is limited, this work develops an animation to real-world pipeline that enables dynamic bipedal robots that can twist, wiggle, and walk to behave with emotions.

So that’s where Cassie’s eyes go.

[ Berkeley ]

Now that the DARPA SubT Cave Circuit is all virtual, here’s a good reminder of how it’ll work.

[ SubT ]

Since July 20, anyone 11+ years of age must wear a mask in closed public places in France. This measure also is highly recommended in many European, African and Persian Gulf countries. To support businesses and public places, SoftBank Robotics Europe unveils a new feature with Pepper: AI Face Mask Detection.

[ Softbank ]

University of Michigan researchers are developing new origami inspired methods for designing, fabricating and actuating micro-robots using heat.These improvements will expand the mechanical capabilities of the tiny bots, allowing them to fold into more complex shapes.

[ University of Michigan ]

Suzumori Endo Lab, Tokyo Tech has created various types of IPMC robots. Those robots are fabricated by novel 3D fabrication methods.

[ Suzimori Endo Lab ]

The most explode-y of drones manages not to explode this time.

[ SpaceX ]

At Amazon, we’re constantly innovating to support our employees, customers, and communities as effectively as possible. As our fulfillment and delivery teams have been hard at work supplying customers with items during the pandemic, Amazon’s robotics team has been working behind the scenes to re-engineer bots and processes to increase safety in our fulfillment centers.

While some folks are able to do their jobs at home with just a laptop and internet connection, it’s not that simple for other employees at Amazon, including those who spend their days building and testing robots. Some engineers have turned their homes into R&D labs to continue building these new technologies to better serve our customers and employees. Their creativity and resourcefulness to keep our important programs going is inspiring.

[ Amazon ]

Australian Army soldiers from 2nd/14th Light Horse Regiment (Queensland Mounted Infantry) demonstrated the PD-100 Black Hornet Nano unmanned aircraft vehicle during a training exercise at Shoalwater Bay Training Area, Queensland, on 4 May 2018.

This robot has been around for a long time—maybe 10 years or more? It makes you wonder what the next generation will look like, and if they can manage to make it even smaller.

[ FLIR ]

Event-based cameras are bio-inspired vision sensors whose pixels work independently from each other and respond asynchronously to brightness changes, with microsecond resolution. Their advantages make it possible to tackle challenging scenarios in robotics, such as high-speed and high dynamic range scenes. We present a solution to the problem of visual odometry from the data acquired by a stereo event-based camera rig.

[ Paper ] via [ HKUST ]

Emys can help keep kindergarteners sitting still for a long time, which is not small feat!

[ Emys ]

Introducing the RoboMaster EP Core, an advanced educational robot that was built to take learning to the next level and provides an all-in-one solution for STEAM-based classrooms everywhere, offering AI and programming projects for students of all ages and experience levels.

[ DJI ]

This Dutch food company Heemskerk uses ABB robots to automate their order picking. Their new solution reduces the amount of time the fresh produce spends in the supply chain, extending its shelf life, minimizing wastage, and creating a more sustainable solution for the fresh food industry.

[ ABB ]

This week’s episode of Pass the Torque features NASA’s Satellite Servicing Projects Division (NExIS) Robotics Engineer, Zakiya Tomlinson.

[ NASA ]

Massachusetts has been challenging Silicon Valley as the robotics capital of the United States. They’re not winning, yet. But they’re catching up.

[ MassTech ]

San Francisco-based Formant is letting anyone remotely take its Spot robot for a walk. Watch The Robot Report editors, based in Boston, take Spot for a walk around Golden Gate Park.

You can apply for this experience through Formant at the link below.

[ Formant ] via [ TRR ]

Thanks Steve!

An Institute for Advanced Study Seminar on “Theoretical Machine Learning,” featuring Peter Stone from UT Austin.

For autonomous robots to operate in the open, dynamically changing world, they will need to be able to learn a robust set of skills from relatively little experience. This talk begins by introducing Grounded Simulation Learning as a way to bridge the so-called reality gap between simulators and the real world in order to enable transfer learning from simulation to a real robot. It then introduces two new algorithms for imitation learning from observation that enable a robot to mimic demonstrated skills from state-only trajectories, without any knowledge of the actions selected by the demonstrator. Connections to theoretical advances in off-policy reinforcement learning will be highlighted throughout.

[ IAS ] Continue reading

Posted in Human Robots

#437728 A Battery That’s Tough Enough To ...

Batteries can add considerable mass to any design, and they have to be supported using a sufficiently strong structure, which can add significant mass of its own. Now researchers at the University of Michigan have designed a structural zinc-air battery, one that integrates directly into the machine that it powers and serves as a load-bearing part.

That feature saves weight and thus increases effective storage capacity, adding to the already hefty energy density of the zinc-air chemistry. And the very elements that make the battery physically strong help contain the chemistry’s longstanding tendency to degrade over many hundreds of charge-discharge cycles.

The research is being published today in Science Robotics.

Nicholas Kotov, a professor of chemical engineer, is the leader of the project. He would not say how many watt-hours his prototype stores per gram, but he did note that zinc air—because it draw on ambient air for its electricity-producing reactions—is inherently about three times as energy-dense as lithium-ion cells. And, because using the battery as a structural part means dispensing with an interior battery pack, you could free up perhaps 20 percent of a machine’s interior. Along with other factors the new battery could in principle provide as much as 72 times the energy per unit of volume (not of mass) as today’s lithium-ion workhorses.

Illustration: Alice Kitterman/Science Robotics

“It’s not as if we invented something that was there before us,” Kotov says. ”I look in the mirror and I see my layer of fat—that’s for the storage of energy, but it also serves other purposes,” like keeping you warm in the wintertime. (A similar advance occurred in rocketry when designers learned how to make some liquid propellant tanks load bearing, eliminating the mass penalty of having separate external hull and internal tank walls.)

Others have spoken of putting batteries, including the lithium-ion kind, into load-bearing parts in vehicles. Ford, BMW, and Airbus, for instance, have expressed interest in the idea. The main problem to overcome is the tradeoff in load-bearing batteries between electrochemical performance and mechanical strength.

Image: Kotov Lab/University of Michigan

Key to the battery's physical toughness and to its long life cycle is the nanofiber membrane, made of Kevlar.

The Michigan group get both qualities by using a solid electrolyte (which can’t leak under stress) and by covering the electrodes with a membrane whose nanostructure of fibers is derived from Kevlar. That makes the membrane tough enough to suppress the growth of dendrites—branching fibers of metal that tend to form on an electrode with every charge-discharge cycle and which degrade the battery.

The Kevlar need not be purchased new but can be salvaged from discarded body armor. Other manufacturing steps should be easy, too, Kotov says. He has only just begun to talk to potential commercial partners, but he says there’s no reason why his battery couldn’t hit the market in the next three or four years.

Drones and other autonomous robots might be the most logical first application because their range is so severely chained to their battery capacity. Also, because such robots don’t carry people about, they face less of a hurdle from safety regulators leery of a fundamentally new battery type.

“And it’s not just about the big Amazon robots but also very small ones,” Kotov says. “Energy storage is a very significant issue for small and flexible soft robots.”

Here’s a video showing how Kotov’s lab has used batteries to form the “exoskeleton” of robots that scuttle like worms or scorpions. Continue reading

Posted in Human Robots

#437721 Video Friday: Child Robot Learning to ...

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.

We first met Ibuki, Hiroshi Ishiguro’s latest humanoid robot, a couple of years ago. A recent video shows how Ishiguro and his team are teaching the robot to express its emotional state through gait and body posture while moving.

This paper presents a subjective evaluation of the emotions of a wheeled mobile humanoid robot expressing emotions during movement by replicating human gait-induced upper body motion. For this purpose, we proposed the robot equipped with a vertical oscillation mechanism that generates such motion by focusing on human center-of-mass trajectory. In the experiment, participants watched videos of the robot’s different emotional gait-induced upper body motions, and assess the type of emotion shown, and their confidence level in their answer.

[ Hiroshi Ishiguro Lab ] via [ RobotStart ]

ICYMI: This is a zinc-air battery made partly of Kevlar that can be used to support weight, not just add to it.

Like biological fat reserves store energy in animals, a new rechargeable zinc battery integrates into the structure of a robot to provide much more energy, a team led by the University of Michigan has shown.

The new battery works by passing hydroxide ions between a zinc electrode and the air side through an electrolyte membrane. That membrane is partly a network of aramid nanofibers—the carbon-based fibers found in Kevlar vests—and a new water-based polymer gel. The gel helps shuttle the hydroxide ions between the electrodes. Made with cheap, abundant and largely nontoxic materials, the battery is more environmentally friendly than those currently in use. The gel and aramid nanofibers will not catch fire if the battery is damaged, unlike the flammable electrolyte in lithium ion batteries. The aramid nanofibers could be upcycled from retired body armor.

[ University of Michigan ]

In what they say is the first large-scale study of the interactions between sound and robotic action, researchers at CMU’s Robotics Institute found that sounds could help a robot differentiate between objects, such as a metal screwdriver and a metal wrench. Hearing also could help robots determine what type of action caused a sound and help them use sounds to predict the physical properties of new objects.

[ CMU ]

Captured on Aug. 11 during the second rehearsal of the OSIRIS-REx mission’s sample collection event, this series of images shows the SamCam imager’s field of view as the NASA spacecraft approaches asteroid Bennu’s surface. The rehearsal brought the spacecraft through the first three maneuvers of the sampling sequence to a point approximately 131 feet (40 meters) above the surface, after which the spacecraft performed a back-away burn.

These images were captured over a 13.5-minute period. The imaging sequence begins at approximately 420 feet (128 meters) above the surface – before the spacecraft executes the “Checkpoint” maneuver – and runs through to the “Matchpoint” maneuver, with the last image taken approximately 144 feet (44 meters) above the surface of Bennu.

[ NASA ]

The DARPA AlphaDogfight Trials Final Event took place yesterday; the livestream is like 5 hours long, but you can skip ahead to 4:39 ish to see the AI winner take on a human F-16 pilot in simulation.

Some things to keep in mind about the result: The AI had perfect situational knowledge while the human pilot had to use eyeballs, and in particular, the AI did very well at lining up its (virtual) gun with the human during fast passing maneuvers, which is the sort of thing that autonomous systems excel at but is not necessarily reflective of better strategy.

[ DARPA ]

Coming soon from Clearpath Robotics!

[ Clearpath ]

This video introduces Preferred Networks’ Hand type A, a tendon-driven robot gripper with passively switchable underactuated surface.

[ Preferred Networks ]

CYBATHLON 2020 will take place on 13 – 14 November 2020 – at the teams’ home bases. They will set up their infrastructure for the competition and film their races. Instead of starting directly next to each other, the pilots will start individually and under the supervision of CYBATHLON officials. From Zurich, the competitions will be broadcast through a new platform in a unique live programme.

[ Cybathlon ]

In this project, we consider the task of autonomous car racing in the top-selling car racing game Gran Turismo Sport. Gran Turismo Sport is known for its detailed physics simulation of various cars and tracks. Our approach makes use of maximum-entropy deep reinforcement learning and a new reward design to train a sensorimotor policy to complete a given race track as fast as possible. We evaluate our approach in three different time trial settings with different cars and tracks. Our results show that the obtained controllers not only beat the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of personal best lap times of over 50,000 human drivers.

[ UZH ]

With the help of the software pitasc from Fraunhofer IPA, an assembly task is no longer programmed point by point, but workpiece-related. Thus, pitasc adapts the assembly process itself for new product variants with the help of updated parameters.

[ Fraunhofer ]

In this video, a multi-material robot simulator is used to design a shape-changing robot, which is then transferred to physical hardware. The simulated and real robots can use shape change to switch between rolling gaits and inchworm gaits, to locomote in multiple environments.

[ Yale ]

This work presents a novel loco-manipulation control framework for the execution of complex tasks with kinodynamic constraints using mobile manipulators. As a representative example, we consider the handling and re-positioning of pallet jacks in unstructured environments. While these results reveal with a proof-of- concept the effectiveness of the proposed framework, they also demonstrate the high potential of mobile manipulators for relieving human workers from such repetitive and labor intensive tasks. We believe that this extended functionality can contribute to increasing the usability of mobile manipulators in different application scenarios.

[ Paper ] via [ IIT ]

I don’t know why this dinosaur ice cream serving robot needs to blow smoke out of its nose, but I like it.

[ Connected Robotics ] via [ RobotStart ]

Guardian S remote visual inspection and surveillance robots make laying cable runs in confined or hard to reach spaces easy. With advanced maneuverability and the ability to climb vertical, ferrous surfaces, the robot reaches areas that are not always easily accessible.

[ Sarcos ]

Looks like the company that bought Anki is working on an add-on to let cars charge while they drive.

[ Digital Dream Labs ]

Chris Atkeson gives a brief talk for the CMU Robotics Institute orientation.

[ CMU RI ]

A UofT Robotics Seminar, featuring Russ Tedrake from MIT and TRI on “Feedback Control for Manipulation.”

Control theory has an answer for just about everything, but seems to fall short when it comes to closing a feedback loop using a camera, dealing with the dynamics of contact, and reasoning about robustness over the distribution of tasks one might find in the kitchen. Recent examples from RL and imitation learning demonstrate great promise, but don’t leverage the rigorous tools from systems theory. I’d like to discuss why, and describe some recent results of closing feedback loops from pixels for “category-level” robot manipulation.

[ UofT ] 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