Tag Archives: emotional

#436190 What Is the Uncanny Valley?

Posted on November 21, 2019 by Android

Have you ever encountered a lifelike humanoid robot or a realistic computer-generated face that seem a bit off or unsettling, though you can’t quite explain why?

Take for instance AVA, one of the “digital humans” created by New Zealand tech startup Soul Machines as an on-screen avatar for Autodesk. Watching a lifelike digital being such as AVA can be both fascinating and disconcerting. AVA expresses empathy through her demeanor and movements: slightly raised brows, a tilt of the head, a nod.

By meticulously rendering every lash and line in its avatars, Soul Machines aimed to create a digital human that is virtually undistinguishable from a real one. But to many, rather than looking natural, AVA actually looks creepy. There’s something about it being almost human but not quite that can make people uneasy.

Like AVA, many other ultra-realistic avatars, androids, and animated characters appear stuck in a disturbing in-between world: They are so lifelike and yet they are not “right.” This void of strangeness is known as the uncanny valley.

Uncanny Valley: Definition and History
The uncanny valley is a concept first introduced in the 1970s by Masahiro Mori, then a professor at the Tokyo Institute of Technology. The term describes Mori’s observation that as robots appear more humanlike, they become more appealing—but only up to a certain point. Upon reaching the uncanny valley, our affinity descends into a feeling of strangeness, a sense of unease, and a tendency to be scared or freaked out.

Image: Masahiro Mori

The uncanny valley as depicted in Masahiro Mori’s original graph: As a robot’s human likeness [horizontal axis] increases, our affinity towards the robot [vertical axis] increases too, but only up to a certain point. For some lifelike robots, our response to them plunges, and they appear repulsive or creepy. That’s the uncanny valley.

In his seminal essay for Japanese journal Energy, Mori wrote:

I have noticed that, in climbing toward the goal of making robots appear human, our affinity for them increases until we come to a valley, which I call the uncanny valley.

Later in the essay, Mori describes the uncanny valley by using an example—the first prosthetic hands:

One might say that the prosthetic hand has achieved a degree of resemblance to the human form, perhaps on a par with false teeth. However, when we realize the hand, which at first site looked real, is in fact artificial, we experience an eerie sensation. For example, we could be startled during a handshake by its limp boneless grip together with its texture and coldness. When this happens, we lose our sense of affinity, and the hand becomes uncanny.

In an interview with IEEE Spectrum, Mori explained how he came up with the idea for the uncanny valley:

“Since I was a child, I have never liked looking at wax figures. They looked somewhat creepy to me. At that time, electronic prosthetic hands were being developed, and they triggered in me the same kind of sensation. These experiences had made me start thinking about robots in general, which led me to write that essay. The uncanny valley was my intuition. It was one of my ideas.”

Uncanny Valley Examples
To better illustrate how the uncanny valley works, here are some examples of the phenomenon. Prepare to be freaked out.

1. Telenoid

Photo: Hiroshi Ishiguro/Osaka University/ATR

Taking the top spot in the “creepiest” rankings of IEEE Spectrum’s Robots Guide, Telenoid is a robotic communication device designed by Japanese roboticist Hiroshi Ishiguro. Its bald head, lifeless face, and lack of limbs make it seem more alien than human.

2. Diego-san

Photo: Andrew Oh/Javier Movellan/Calit2

Engineers and roboticists at the University of California San Diego’s Machine Perception Lab developed this robot baby to help parents better communicate with their infants. At 1.2 meters (4 feet) tall and weighing 30 kilograms (66 pounds), Diego-san is a big baby—bigger than an average 1-year-old child.

“Even though the facial expression is sophisticated and intuitive in this infant robot, I still perceive a false smile when I’m expecting the baby to appear happy,” says Angela Tinwell, a senior lecturer at the University of Bolton in the U.K. and author of The Uncanny Valley in Games and Animation. “This, along with a lack of detail in the eyes and forehead, can make the baby appear vacant and creepy, so I would want to avoid those ‘dead eyes’ rather than interacting with Diego-san.”

​3. Geminoid HI

Photo: Osaka University/ATR/Kokoro

Another one of Ishiguro’s creations, Geminoid HI is his android replica. He even took hair from his own scalp to put onto his robot twin. Ishiguro says he created Geminoid HI to better understand what it means to be human.

4. Sophia

Photo: Mikhail Tereshchenko/TASS/Getty Images

Designed by David Hanson of Hanson Robotics, Sophia is one of the most famous humanoid robots. Like Soul Machines’ AVA, Sophia displays a range of emotional expressions and is equipped with natural language processing capabilities.

5. Anthropomorphized felines

The uncanny valley doesn’t only happen with robots that adopt a human form. The 2019 live-action versions of the animated film The Lion King and the musical Cats brought the uncanny valley to the forefront of pop culture. To some fans, the photorealistic computer animations of talking lions and singing cats that mimic human movements were just creepy.

Are you feeling that eerie sensation yet?

Uncanny Valley: Science or Pseudoscience?
Despite our continued fascination with the uncanny valley, its validity as a scientific concept is highly debated. The uncanny valley wasn’t actually proposed as a scientific concept, yet has often been criticized in that light.

Mori himself said in his IEEE Spectrum interview that he didn’t explore the concept from a rigorous scientific perspective but as more of a guideline for robot designers:

Pointing out the existence of the uncanny valley was more of a piece of advice from me to people who design robots rather than a scientific statement.

Karl MacDorman, an associate professor of human-computer interaction at Indiana University who has long studied the uncanny valley, interprets the classic graph not as expressing Mori’s theory but as a heuristic for learning the concept and organizing observations.

“I believe his theory is instead expressed by his examples, which show that a mismatch in the human likeness of appearance and touch or appearance and motion can elicit a feeling of eeriness,” MacDorman says. “In my own experiments, I have consistently reproduced this effect within and across sense modalities. For example, a mismatch in the human realism of the features of a face heightens eeriness; a robot with a human voice or a human with a robotic voice is eerie.”

How to Avoid the Uncanny Valley
Unless you intend to create creepy characters or evoke a feeling of unease, you can follow certain design principles to avoid the uncanny valley. “The effect can be reduced by not creating robots or computer-animated characters that combine features on different sides of a boundary—for example, human and nonhuman, living and nonliving, or real and artificial,” MacDorman says.

To make a robot or avatar more realistic and move it beyond the valley, Tinwell says to ensure that a character’s facial expressions match its emotive tones of speech, and that its body movements are responsive and reflect its hypothetical emotional state. Special attention must also be paid to facial elements such as the forehead, eyes, and mouth, which depict the complexities of emotion and thought. “The mouth must be modeled and animated correctly so the character doesn’t appear aggressive or portray a ‘false smile’ when they should be genuinely happy,” she says.

For Christoph Bartneck, an associate professor at the University of Canterbury in New Zealand, the goal is not to avoid the uncanny valley, but to avoid bad character animations or behaviors, stressing the importance of matching the appearance of a robot with its ability. “We’re trained to spot even the slightest divergence from ‘normal’ human movements or behavior,” he says. “Hence, we often fail in creating highly realistic, humanlike characters.”

But he warns that the uncanny valley appears to be more of an uncanny cliff. “We find the likability to increase and then crash once robots become humanlike,” he says. “But we have never observed them ever coming out of the valley. You fall off and that’s it.” Continue reading →

Posted in Human Robots

#436184 Why People Demanded Privacy to Confide ...

Posted on November 21, 2019 by Android

This is part four of a six-part series on the history of natural language processing.

Between 1964 and 1966, Joseph Weizenbaum, a German American computer scientist at MIT’s artificial intelligence lab, developed the first-ever chatbot [PDF].

While there were already some rudimentary digital language generators in existence—programs that could spit out somewhat coherent lines of text—Weizenbaum’s program was the first designed explicitly for interactions with humans. The user could type in some statement or set of statements in their normal language, press enter, and receive a response from the machine. As Weizenbaum explained, his program made “certain kinds of natural-language conversation between man and computer possible.”

He named the program Eliza after Eliza Doolittle, the working-class hero of George Bernard Shaw’s Pygmalion who learns how to talk with an upper-class accent. The new Eliza was written for the 36-bit IBM 7094, an early transistorized mainframe computer, in a programming language that Weizenbaum developed called MAD-SLIP.

Because computer time was a valuable resource, Eliza could only be run via a time-sharing system; the user interacted with the program remotely via an electric typewriter and printer. When the user typed in a sentence and pressed enter, a message was sent to the mainframe computer. Eliza scanned the message for the presence of a keyword and used it in a new sentence to form a response that was sent back, printed out, and read by the user.

To encourage ongoing dialogue, Weizenbaum designed Eliza to simulate the type of conversational style used by a Rogerian psychoanalyst. The program would take something the user said and reflect it back with a question, as can be seen in the example below. (Notice how the program takes keywords like “boyfriend” and “depressed” and re-uses them.)

Human: Men are all alike.

Computer: IN WHAT WAY?

Human: They're always bugging us about something or other.

Computer: CAN YOU THINK OF A SPECIFIC EXAMPLE?

Human: Well, my boyfriend made me come here.

Computer: YOUR BOYFRIEND MADE YOU COME HERE

Human: He says I'm depressed much of the time.

Computer: I AM SORRY TO HEAR YOU ARE DEPRESSED

Human: It's true. I am unhappy.

Weizenbaum chose this mode of dialogue for Eliza because it gave the impression that the computer understood what was being said without having to offer anything new to the conversation. It created the illusion of comprehension and engagement in a mere 200 lines of code.

To test Eliza’s capacity to engage an interlocutor, Weizenbaum invited students and colleagues into his office and let them chat with the machine while he looked on. He noticed, with some concern, that during their brief interactions with Eliza, many users began forming emotional attachments to the algorithm. They would open up to the machine and confess problems they were facing in their lives and relationships.

During their brief interactions with Eliza, many users began forming emotional attachments to the algorithm.

Even more surprising was that this sense of intimacy persisted even after Weizenbaum described how the machine worked and explained that it didn’t really understand anything that was being said. Weizenbaum was most troubled when his secretary, who had watched him build the program from scratch over many months, insisted that he leave the room so she could talk to Eliza in private.

For Weizenbaum, this experiment with Eliza made him question an idea that Alan Turing had proposed in 1950 about machine intelligence. In his paper, entitled “Computing Machinery and Intelligence,” Turing suggested that if a computer could conduct a convincingly human conversation in text, one could assume it was intelligent—an idea that became the basis of the famous Turing Test.

But Eliza demonstrated that convincing communication between a human and a machine could take place even if comprehension only flowed from one side: The simulation of intelligence, rather than intelligence itself, was enough to fool people. Weizenbaum called this the Eliza effect, and believed it was a type of “delusional thinking” that humanity would collectively suffer from in the digital age. This insight was a profound shock for Weizenbaum, and one that came to define his intellectual trajectory over the next decade.

The simulation of intelligence, rather than intelligence itself, was enough to fool people.

In 1976, he published Computing Power and Human Reason: From Judgment to Calculation [PDF], which offered a long meditation on why people are willing to believe that a simple machine might be able to understand their complex human emotions.

In this book, he argues that the Eliza effect signifies a broader pathology afflicting “modern man.” In a world conquered by science, technology, and capitalism, people had grown accustomed to viewing themselves as isolated cogs in a large and uncaring machine. In such a diminished social world, Weizenbaum reasoned, people had grown so desperate for connection that they put aside their reason and judgment in order to believe that a program could care about their problems.

Weizenbaum spent the rest of his life developing this humanistic critique of artificial intelligence and digital technology. His mission was to remind people that their machines were not as smart as they were often said to be. And that even though it sometimes appeared as though they could talk, they were never really listening.

This is the fourth installment of a six-part series on the history of natural language processing. Last week’s post described Andrey Markov and Claude Shannon’s painstaking efforts to create statistical models of language for text generation. Come back next Monday for part five, “In 2016, Microsoft’s Racist Chatbot Revealed the Dangers of Conversation.”

You can also check out our prior series on the untold history of AI. Continue reading →

Posted in Human Robots

#436149 Blue Frog Robotics Answers (Some of) Our ...

Posted on October 29, 2019 by Android

In September of 2015, Buddy the social home robot closed its Indiegogo crowdfunding campaign more than 600 percent over its funding goal. A thousand people pledged for a robot originally scheduled to be delivered in December of 2016. But nearly three years later, the future of Buddy is still unclear. Last May, Blue Frog Robotics asked for forgiveness from its backers and announced the launch of an “equity crowdfunding campaign” to try to raise the additional funding necessary to deliver the robot in April of 2020.

By the time the crowdfunding campaign launched in August, the delivery date had slipped again, to September 2020, even as Blue Frog attempted to draw investors by estimating that sales of Buddy would “increase from 2000 robots in 2020 to 20,000 in 2023.” Blue Frog’s most recent communication with backers, in September, mentions a new CTO and a North American office, but does little to reassure backers of Buddy that they’ll ever be receiving their robot.

Backers of the robot are understandably concerned about the future of Buddy, so we sent a series of questions to the founder and CEO of Blue Frog Robotics, Rodolphe Hasselvander.

We’ve edited this interview slightly for clarity, but we should also note that Hasselvander was unable to provide answers to every question. In particular, we asked for some basic information about Blue Frog’s near-term financial plans, on which the entire future of Buddy seems to depend. We’ve left those questions in the interview anyway, along with Hasselvander’s response.

1. At this point, how much additional funding is necessary to deliver Buddy to backers?
2. Assuming funding is successful, when can backers expect to receive Buddy?
3. What happens if the fundraising goal is not met?
4. You estimate that sales of Buddy will increase 10x over three years. What is this estimate based on?

Rodolphe Hasselvander: Regarding the questions 1-4, unfortunately, as we are fundraising in a Regulation D, we do not comment on prospect, customer data, sales forecasts, or figures. Please refer to our press release here to have information about the fundraising.

5. Do you feel that you are currently being transparent enough about this process to satisfy backers?
6. Buddy’s launch date has moved from April 2020 to September 2020 over the last four months. Why should backers remain confident about Buddy’s schedule?

Since the last newsletter, we haven’t changed our communication, the backers will be the first to receive their Buddy, and we plan an official launch in September 2020.

7. What is the goal of My Buddy World?

At Blue Frog, we think that matching a great product with a big market can only happen through continual experimentation, iteration and incorporation of customer feedback. That’s why we created the forum My Buddy World. It has been designed for our Buddy Community to join us, discuss the world’s first emotional robot, and create with us. The objective is to deepen our conversation with Buddy’s fans and users, stay agile in testing our hypothesis and validate our product-market fit. We trust the value of collaboration. Behind Buddy, there is a team of roboticists, engineers, and programmers that are eager to know more about our consumers’ needs and are excited to work with them to create the perfect human/robot experience.

8. How is the current version of Buddy different from the 2015 version that backers pledged for during the successful crowdfunding campaign, in both hardware and software?

We have completely revised some parts of Buddy as well as replaced and/or added more accurate and reliable components to ensure we fully satisfy our customers’ requirements for a mature and high-quality robot from day one. We sourced more innovative components to make sure that Buddy has the most up-to-date technologies such as adding four microphones, a high def thermal matrix, a 3D camera, an 8-megapixel RGB camera, time-of-flight sensors, and touch sensors.
If you want more info, we just posted an article about what is Buddy here.

9. Will the version of Buddy that ships to backers in 2020 do everything that that was shown in the original crowdfunding video?

Concerning the capabilities of Buddy regarding the video published on YouTube, I confirm that Buddy will be able to do everything you can see, like patrol autonomously and secure your home, telepresence, mathematics applications, interactive stories for children, IoT/smart home management, face recognition, alarm clock, reminder, message/photo sharing, music, hands free call, people following, games like hide and seek (and more). In addition, everyone will be able to create their own apps thanks to the “BuddyLab” application.

10. What makes you confident that Buddy will be successful when Jibo, Kuri, and other social robots have not?

Consumer robotics is a new market. Some people think it is a tough one. But we, at Blue Frog Robotics, believe it is a path of learning, understanding, and finding new ways to serve consumers. Here are the five key factors that will make Buddy successful.

1) A market-fit robot

Blue Frog Robotics is a consumer-centric company. We know that a successful business model and a compelling fit to market Buddy must come up from solving consumers’ frustrations and problems in a way that’s new and exciting. We started from there.

By leveraged existing research and syndicated consumer data sets to understand our customers’ needs and aspirations, we get that creating a robot is not about the best tech innovation and features, but always about how well technology becomes a service to one’s basic human needs and assets: convenience, connection, security, fun, self-improvement, and time. To answer to these consumers’ needs and wants, we designed an all-in-one robot with four vital capabilities: intelligence, emotionality, mobility, and customization.

With his multi-purpose brain, he addresses a broad range of needs in modern-day life, from securing homes to carrying out his owners’ daily activities, from helping people with disabilities to educating children, from entertaining to just becoming a robot friend.

Buddy is a disruptive innovative robot that is about to transform the way we live, learn, utilize information, play, and even care about our health.
2) Endless possibilities

One of the major advantages of Buddy is his adaptability. Beyond to be adorable, playful, talkative, and to accompany anyone in their daily life at home whether you are comfortable with technology or not, he offers via his platform applications to engage his owners in a wide range of activities. From fitness to cooking, from health monitoring to education, from games to meditation, the combination of intelligence, sensors, mobility, multi-touch panel opens endless possibilities for consumers and organizations to adapt their Buddy to their own needs.
3) An affordable price

Buddy will be the first robot combining smart, social, and mobile capabilities and a developed platform with a personality to enter the U.S. market at affordable price.

Our competitors are social or assistant robots but rarely both. Competitors differentiate themselves by features: mobile, non-mobile; by shapes: humanoid or not; by skills: social versus smart; targeting a specific domain like entertainment, retail assistant, eldercare, or education for children; and by price. Regarding our six competitors: Moorebot, Elli-Q, and Olly are not mobile; Lynx and Nao are in toy category; Pepper is above $10k targeting B2B market; and finally, Temi can’t be considered an emotional robot.
Buddy remains highly differentiated as an all-in-one, best of his class experience, covering the needs for social interactions and assistance of his owners at each stage of their life at an affordable price.

The price range of Buddy will be between US $1700 and $2000.

4) A winning business model

Buddy’s great business model combines hardware, software, and services, and provides game-changing convenience for consumers, organizations, and developers.

Buddy offers a multi-sided value proposition focused on three vertical markets: direct consumers, corporations (healthcare, education, hospitality), and developers. The model creates engagement and sustained usage and produces stable and diverse cash flow.
5) A Passion for people and technology

From day one, we have always believed in the power of our dream: To bring the services and the fun of an emotional robot in every house, every hospital, in every care house. Each day, we refuse to think that we are stuck or limited; we work hard to make Buddy a reality that will help people all over the world and make them smile.

While we certainly appreciate Hasselvander’s consistent optimism and obvious enthusiasm, we’re obligated to point out that some of our most important questions were not directly answered. We haven’t learned anything that makes us all that much more confident that Blue Frog will be able to successfully deliver Buddy this time. Hasselvander also didn’t address our specific question about whether he feels like Blue Frog’s communication strategy with backers has been adequate, which is particularly relevant considering that over the four months between the last two newsletters, Buddy’s launch date slipped by six months.

At this point, all we can do is hope that the strategy Blue Frog has chosen will be successful. We’ll let you know if as soon as we learn more.

[ Buddy ] Continue reading →

Posted in Human Robots

#435742 This ‘Useless’ Social Robot ...

Posted on September 19, 2019 by Android

The recent high profile failures of some home social robots (and the companies behind them) have made it even more challenging than it was before to develop robots in that space. And it was challenging enough to begin with—making a robot that can autonomous interact with random humans in their homes over a long period of time for a price that people can afford is extraordinarily difficult. However, the massive amount of initial interest in robots like Jibo, Kuri, Vector, and Buddy prove that people do want these things, or at least think they do, and while that’s the case, there’s incentive for other companies to give social home robots a try.

One of those companies is Zoetic, founded in 2107 by Mita Yun and Jitu Das, both ex-Googlers. Their robot, Kiki, is more or less exactly what you’d expect from a social home robot: It’s cute, white, roundish, has big eyes, promises that it will be your “robot sidekick,” and is not cheap: It’s on Kicksterter for $800. Kiki is among what appears to be a sort of tentative second wave of social home robots, where designers have (presumably) had a chance to take everything that they learned from the social home robot pioneers and use it to make things better this time around.

Kiki’s Kickstarter video is, again, more or less exactly what you’d expect from a social home robot crowdfunding campaign:

We won’t get into all of the details on Kiki in this article (the Kickstarter page has tons of information), but a few distinguishing features:

Each Kiki will develop its own personality over time through its daily interactions with its owner, other people, and other Kikis.
Interacting with Kiki is more abstract than with most robots—it can understand some specific words and phrases, and will occasionally use a few specific words or two, but otherwise it’s mostly listening to your tone of voice and responding with sounds rather than speech.
Kiki doesn’t move on its own, but it can operate for up to two hours away from its charging dock.
Depending on how your treat Kiki, it can get depressed or neurotic. It also needs to be fed, which you can do by drawing different kinds of food in the app.
Everything Kiki does runs on-board the robot. It has Wi-Fi connectivity for updates, but doesn’t rely on the cloud for anything in real-time, meaning that your data stays on the robot and that the robot will continue to function even if its remote service shuts down.

It’s hard to say whether features like these are unique enough to help Kiki be successful where other social home robots haven’t been, so we spoke with Zoetic co-founder Mita Yun and asked her why she believes that Kiki is going to be the social home robot that makes it.

IEEE Spectrum: What’s your background?

Mita Yun: I was an only child growing up, and so I always wanted something like Doraemon or Totoro. Something that when you come home it’s there to greet you, not just because it’s programmed to do that but because it’s actually actively happy to see you, and only you. I was so interested in this that I went to study robotics at CMU and then after I graduated I joined Google and worked there for five years. I tended to go for the more risky and more fun projects, but they always got cancelled—the first project I joined was called Android at Home, and then I joined Google Glass, and then I joined a team called Robots for Kids. That project was building educational robots, and then I just realized that when we’re adding technology to something, to a product, we’re actually taking the life away somehow, and the kids were more connected with stuffed animals compared to the educational robots we were building. That project was also cancelled, and in 2017, I left with a coworker of mine (Jitu Das) to bring this dream into reality. And now we’re building Kiki.

“Jibo was Alexa plus cuteness equals $800, and I feel like that equation doesn’t work for most people, and that eventually killed the company. So, for Kiki, we are actually building something very different. We’re building something that’s completely useless”
—Mita Yun, Zoetic

You started working on Kiki in 2017, when things were already getting challenging for Jibo—why did you decide to start developing a social home robot at that point?

I thought Jibo was great. It had a special magical way of moving, and it was such a new idea that you could have this robot with embodiment and it can actually be your assistant. The problem with Jibo, in my opinion, was that it took too long to fulfill the orders. It took them three to four years to actually manufacture, because it was a very complex piece of hardware, and then during that period of time Alexa and Google Home came out, and they started selling these voice systems for $30 and then you have Jibo for $800. Jibo was Alexa plus cuteness equals $800, and I feel like that equation doesn’t work for most people, and that eventually killed the company. So, for Kiki, we are actually building something very different. We’re building something that’s completely useless.

Can you elaborate on “completely useless?”

I feel like people are initially connected with robots because they remind them of a character. And it’s the closest we can get to a character other than an organic character like an animal. So we’re connected to a character like when we have a robot in a mall that’s roaming around, even if it looks really ugly, like if it doesn’t have eyes, people still take selfies with it. Why? Because they think it’s a character. And humans are just hardwired to love characters and love stories. With Kiki, we just wanted to build a character that’s alive, we don’t want to have a character do anything super useful.

I understand why other robotics companies are adding Alexa integration to their robots, and I think that’s great. But the dream I had, and the understanding I have about robotics technology, is that for a consumer robot especially, it is very very difficult for the robot to justify its price through usefulness. And then there’s also research showing that the more useless something is, the easier it is to have an emotional connection, so that’s why we want to keep Kiki very useless.

What kind of character are you creating with Kiki?

The whole design principle around Kiki is we want to make it a very vulnerable character. In terms of its status at home, it’s not going to be higher or equal status as the owner, but slightly lower status than the human, and it’s vulnerable and needs you to take care of it in order to grow up into a good personality robot.

We don’t let Kiki speak full English sentences, because whenever it does that, people are going to think it’s at least as intelligent as a baby, which is impossible for robots at this point. And we also don’t let it move around, because when you have it move around, people are going to think “I’m going to call Kiki’s name, and then Kiki is will come to me.” But that is actually very difficult to build. And then also we don’t have any voice integration so it doesn’t tell you about the stock market price and so on.

Photo: Zoetic

Kiki is designed to be “vulnerable,” and it needs you to take care of it so it can “grow up into a good personality robot,” according to its creators.

That sounds similar to what Mayfield did with Kuri, emphasizing an emotional connection rather than specific functionality.

It is very similar, but one of the key differences from Kuri, I think, is that Kuri started with a Kobuki base, and then it’s wrapped into a cute shell, and they added sounds. So Kuri started with utility in mind—navigation is an important part of Kuri, so they started with that challenge. For Kiki, we started with the eyes. The entire thing started with the character itself.

How will you be able to convince your customers to spend $800 on a robot that you’ve described as “useless” in some ways?

Because it’s useless, it’s actually easier to convince people, because it provides you with an emotional connection. I think Kiki is not a utility-driven product, so the adoption cycle is different. For a functional product, it’s very easy to pick up, because you can justify it by saying “I’m going to pay this much and then my life can become this much more efficient.” But it’s also very easy to be replaced and forgotten. For an emotional-driven product, it’s slower to pick up, but once people actually pick it up, they’re going to be hooked—they get be connected with it, and they’re willing to invest more into taking care of the robot so it will grow up to be smarter.

Maintaining value over time has been another challenge for social home robots. How will you make sure that people don’t get bored with Kiki after a few weeks?

Of course Kiki has limits in what it can do. We can combine the eyes, the facial expression, the motors, and lights and sounds, but is it going to be constantly entertaining? So we think of this as, imagine if a human is actually puppeteering Kiki—can Kiki stay interesting if a human is puppeteering it and interacting with the owner? So I think what makes a robot interesting is not just in the physical expressions, but the part in between that and the robot conveying its intentions and emotions.

For example, if you come into the room and then Kiki decides it will turn the other direction, ignore you, and then you feel like, huh, why did the robot do that to me? Did I do something wrong? And then maybe you will come up to it and you will try to figure out why it did that. So, even though Kiki can only express in four different dimensions, it can still make things very interesting, and then when its strategies change, it makes it feel like a new experience.

There’s also an explore and exploit process going on. Kiki wants to make you smile, and it will try different things. It could try to chase its tail, and if you smile, Kiki learns that this works and will exploit it. But maybe after doing it three times, you no longer find it funny, because you’re bored of it, and then Kiki will observe your reactions and be motivated to explore a new strategy.

Photo: Zoetic

Kiki’s creators are hoping that, with an emotionally engaging robot, it will be easier for people to get attached to it and willing to spend time taking care of it.

A particular risk with crowdfunding a robot like this is setting expectations unreasonably high. The emphasis on personality and emotional engagement with Kiki seems like it may be very difficult for the robot to live up to in practice.

I think we invested more than most robotics companies into really building out Kiki’s personality, because that is the single most important thing to us. For Jibo a lot of the focus was in the assistant, and for Kuri, it’s more in the movement. For Kiki, it’s very much in the personality.

I feel like when most people talk about personality, they’re mainly talking about expression. With Kiki, it’s not just in the expression itself, not just in the voice or the eyes or the output layer, it’s in the layer in between—when Kiki receives input, how will it make decisions about what to do? We actually don’t think the personality of Kiki is categorizable, which is why I feel like Kiki has a deeper implementation of how personalities should work. And you’re right, Kiki doesn’t really understand why you’re feeling a certain way, it just reads your facial expressions. It’s maybe not your best friend, but maybe closer to your little guinea pig robot.

Photo: Zoetic

The team behind Kiki paid particular attention to its eyes, and designed the robot to always face the person that it is interacting with.

Is that where you’d put Kiki on the scale of human to pet?

Kiki is definitely not human, we want to keep it very far away from human. And it’s also not a dog or cat. When we were designing Kiki, we took inspiration from mammals because humans are deeply connected to mammals since we’re mammals ourselves. And specifically we’re connected to predator animals. With prey animals, their eyes are usually on the sides of their heads, because they need to see different angles. A predator animal needs to hunt, they need to focus. Cats and dogs are predator animals. So with Kiki, that’s why we made sure the eyes are on one side of the face and the head can actuate independently from the body and the body can turn so it’s always facing the person that it’s paying attention to.

I feel like Kiki is probably does more than a plant. It does more than a fish, because a fish doesn’t look you in the eyes. It’s not as smart as a cat or a dog, so I would just put it in this guinea pig kind of category.

What have you found so far when running user studies with Kiki?

When we were first designing Kiki we went through a whole series of prototypes. One of the earlier prototypes of Kiki looked like a CRT, like a very old monitor, and when we were testing that with people they didn’t even want to touch it. Kiki’s design inspiration actually came from an airplane, with a very angular, futuristic look, but based on user feedback we made it more round and more friendly to the touch. The lights were another feature request from the users, which adds another layer of expressivity to Kiki, and they wanted to see multiple Kikis working together with different personalities. Users also wanted different looks for Kiki, to make it look like a deer or a unicorn, for example, and we actually did take that into consideration because it doesn’t look like any particular mammal. In the future, you’ll be able to have different ears to make it look like completely different animals.

There has been a lot of user feedback that we didn’t implement—I believe we should observe the users reactions and feedback but not listen to their advice. The users shouldn’t be our product designers, because if you test Kiki with 10 users, eight of them will tell you they want Alexa in it. But we’re never going to add Alexa integration to Kiki because that’s not what it’s meant to do.

While it’s far too early to tell whether Kiki will be a long-term success, the Kickstarter campaign is currently over 95 percent funded with 8 days to go, and 34 robots are still available for a May 2020 delivery.

[ Kickstarter ] Continue reading →

Posted in Human Robots

#435731 Video Friday: NASA Is Sending This ...

Posted on September 18, 2019 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!):

MARSS 2019 – July 1-5, 2019 – Helsinki, Finland
ICRES 2019 – July 29-30, 2019 – London, UK
DARPA SubT Tunnel Circuit – August 15-22, 2019 – Pittsburgh, PA, USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

The big news today is that NASA is sending a robot to Saturn’s moon Titan. A flying robot. The Dragonfly mission will launch in 2026 and arrive in 2034, but you knew that already, because last January, we posted a detailed article about the concept from the Applied Physics Lab at Johns Hopkins University. And now it’s not a concept anymore, yay!

Again, read all the details plus an interview in 2018 article.

[ NASA ]

A robotic gripping arm that uses engineered bacteria to “taste” for a specific chemical has been developed by engineers at the University of California, Davis, and Carnegie Mellon University. The gripper is a proof-of-concept for biologically-based soft robotics.

The new device uses a biosensing module based on E. coli bacteria engineered to respond to the chemical IPTG by producing a fluorescent protein. The bacterial cells reside in wells with a flexible, porous membrane that allows chemicals to enter but keeps the cells inside. This biosensing module is built into the surface of a flexible gripper on a robotic arm, so the gripper can “taste” the environment through its fingers.

When IPTG crosses the membrane into the chamber, the cells fluoresce and electronic circuits inside the module detect the light. The electrical signal travels to the gripper’s control unit, which can decide whether to pick something up or release it.

[ UC Davis ]

The Toyota Research Institute (TRI) is taking on the hard problems in manipulation research toward making human-assist robots reliable and robust. Dr. Russ Tedrake, TRI Vice President of Robotics Research, explains how we are exploring the challenges and addressing the reliability gap by using a robot loading dishes in a dishwasher as an example task.

[ TRI ]

The Tactile Telerobot is the world’s first haptic telerobotic system that transmits realistic touch feedback to an operator located anywhere in the world. It is the product of joint collaboration between Shadow Robot Company, HaptX, and SynTouch. All Nippon Airways funded the project’s initial research and development.

What’s really unique about this is the HaptX tactile feedback system, which is something we’ve been following for several years now. It’s one of the most magical tech experiences I’ve ever had, and you can read about it here and here.

[ HaptX ]

Thanks Andrew!

I love how snake robots can emulate some of the fanciest moves of real snakes, and then also do bonkers things that real snakes never do.

[ Matsuno Lab ]

Here are a couple interesting videos from the Human-Robot Interaction Lab at Tufts.

A robot is instructed to perform an action and cannot do it due to lack of sensors. But when another robot is placed nearby, it can execute the instruction by tacitly tapping into the other robot’s mind and using that robot’s sensors for its own actions. Yes, it’s automatic, and yes, it’s the BORG!

Two Nao robots are instructed to perform a dance and are able to do it right after instruction. Moreover, they can switch roles immediately, and even a third different PR2 robot can perform the dance right away, demonstrating the ability of our DIARC architecture to learn quickly and share the knowledge with any type of robot running the architecture.

Compared to Nao, PR2 just sounds… depressed.

[ HRI Lab ]

This work explores the problem of robot tool construction – creating tools from parts available in the environment. We advance the state-of-the-art in robotic tool construction by introducing an approach that enables the robot to construct a wider range of tools with greater computational efficiency. Specifically, given an action that the robot wishes to accomplish and a set of building parts available to the robot, our approach reasons about the shape of the parts and potential ways of attaching them, generating a ranking of part combinations that the robot then uses to construct and test the target tool. We validate our approach on the construction of five tools using a physical 7-DOF robot arm.

[ RAIL Lab ] via [ RSS ]

We like Magazino’s approach to warehouse picking- constrain the problem to something you can reliably solve, like shoeboxes.

Magazino has announced a new pricing model for their robots. You pay 55k euros for the robot itself, and then after that, all you pay to keep the robot working is 6 cents per pick, so the robot is only costing you money for the work that it actually does.

[ Magazino ]

Thanks Florin!

Human-Robot Collaborations are happening across factories worldwide, yet very few are using it for smaller businesses, due to high costs or the difficulty of customization. Elephant Robotics, a new player from Shenzhen, the Silicon Valley of Asia, has set its sight on helping smaller businesses gain access to smart robotics. They created a Catbot (a collaborative robotic arm) that will offer high efficiency and flexibility to various industries.

The Catbot is set to help from education projects, photography, massaging, to being a personal barista or co-playing a table game. The customizations are endless. To increase the flexibility of usage, the Catbot is extremely easy to program from a high precision task up to covering hefty ground projects.

[ Elephant Robotics ]

Thanks Johnson!

Dronistics, an EPFL spin-off, has been testing out their enclosed delivery drone in the Dominican Republic through a partnership with WeRobotics.

[ WeRobotics ]

QTrobot is an expressive humanoid robot designed to help children with autism spectrum disorder and children with special educational needs in learning new skills. QTrobot uses simple and exaggerated facial expressions combined by interactive games and stories, to help children improve their emotional skills. QTrobot helps children to learn about and better understand the emotions and teach them strategies to handle their emotions more effectively.

[ LuxAI ]

Here’s a typical day in the life of a Tertill solar-powered autonomous weed-destroying robot.

$300, now shipping from Franklin Robotics.

[ Tertill ]

PAL Robotics is excited to announce a new TIAGo with two arms, TIAGo++! After carefully listening to the robotics community needs, we used TIAGo’s modularity to integrate two 7-DoF arms to our mobile manipulator. TIAGo++ can help you swiftly accomplish your research goals, opening endless possibilities in mobile manipulation.

[ PAL Robotics ]

Thanks Jack!

You’ve definitely already met the Cobalt security robot, but Toyota AI Ventures just threw a pile of money at them and would therefore like you to experience this re-introduction:

[ Cobalt Robotics ] via [ Toyota AI ]

ROSIE is a mobile manipulator kit from HEBI Robotics. And if you don’t like ROSIE, the modular nature of HEBI’s hardware means that you can take her apart and make something more interesting.

[ HEBI Robotics ]

Learn about Kawasaki Robotics’ second addition to their line of duAro dual-arm collaborative robots, duAro2. This model offers an extended vertical reach (550 mm) and an increased payload capacity (3 kg/arm).

[ Kawasaki Robotics ]

Drone Delivery Canada has partnered with Peel Region Paramedics to pilot its proprietary drone delivery platform to enable rapid first responder technology via drone with the goal to reduce response time and potentially save lives.

[ Drone Delivery Canada ]

In this week’s episode of Robots in Depth, Per speaks with Harri Ketamo, from Headai.

Harri Ketamo talks about AI and how he aims to mimic human decision making with algorithms. Harri has done a lot of AI for computer games to create opponents that are entertaining to play against. It is easy to develop a very bad or a very good opponent, but designing an opponent that behaves like a human, is entertaining to play against and that you can beat is quite hard. He talks about how AI in computer games is a very important story telling tool and an important part of making a game entertaining to play.

This work led him into other parts of the AI field. Harri thinks that we sometimes have a problem separating what is real from what is the type of story telling he knows from gaming AI. He calls for critical analysis of AI and says that data has to be used to verify AI decisions and results.

[ Robots in Depth ]

Thanks Per! Continue reading →

Posted in Human Robots