Tag Archives: story

#439023 In ‘Klara and the Sun,’ We Glimpse ...

Posted on March 25, 2021 by Android

In a store in the center of an unnamed city, humanoid robots are displayed alongside housewares and magazines. They watch the fast-moving world outside the window, anxiously awaiting the arrival of customers who might buy them and take them home. Among them is Klara, a particularly astute robot who loves the sun and wants to learn as much as possible about humans and the world they live in.

So begins Kazuo Ishiguro’s new novel Klara and the Sun, published earlier this month. The book, told from Klara’s perspective, portrays an eerie future society in which intelligent machines and other advanced technologies have been integrated into daily life, but not everyone is happy about it.

Technological unemployment, the progress of artificial intelligence, inequality, the safety and ethics of gene editing, increasing loneliness and isolation—all of which we’re grappling with today—show up in Ishiguro’s world. It’s like he hit a fast-forward button, mirroring back to us how things might play out if we don’t approach these technologies with caution and foresight.

The wealthy genetically edit or “lift” their children to set them up for success, while the poor have to make do with the regular old brains and bodies bequeathed them by evolution. Lifted and unlifted kids generally don’t mix, and this is just one of many sinister delineations between a new breed of haves and have-nots.

There’s anger about robots’ steady infiltration into everyday life, and questions about how similar their rights should be to those of humans. “First they take the jobs. Then they take the seats at the theater?” one woman fumes.

References to “changes” and “substitutions” allude to an economy where automation has eliminated millions of jobs. While “post-employed” people squat in abandoned buildings and fringe communities arm themselves in preparation for conflict, those whose livelihoods haven’t been destroyed can afford to have live-in housekeepers and buy Artificial Friends (or AFs) for their lonely children.

“The old traditional model that we still live with now—where most of us can get some kind of paid work in exchange for our services or the goods we make—has broken down,” Ishiguro said in a podcast discussion of the novel. “We’re not talking just about the difference between rich and poor getting bigger. We’re talking about a gap appearing between people who participate in society in an obvious way and people who do not.”

He has a point; as much as techno-optimists claim that the economic changes brought by automation and AI will give us all more free time, let us work less, and devote time to our passion projects, how would that actually play out? What would millions of “post-employed” people receiving basic income actually do with their time and energy?

In the novel, we don’t get much of a glimpse of this side of the equation, but we do see how the wealthy live. After a long wait, just as the store manager seems ready to give up on selling her, Klara is chosen by a 14-year-old girl named Josie, the daughter of a woman who wears “high-rank clothes” and lives in a large, sunny home outside the city. Cheerful and kind, Josie suffers from an unspecified illness that periodically flares up and leaves her confined to her bed for days at a time.

Her life seems somewhat bleak, the need for an AF clear. In this future world, the children of the wealthy no longer go to school together, instead studying alone at home on their digital devices. “Interaction meetings” are set up for them to learn to socialize, their parents carefully eavesdropping from the next room and trying not to intervene when there’s conflict or hurt feelings.

Klara does her best to be a friend, aide, and confidante to Josie while continuing to learn about the world around her and decode the mysteries of human behavior. We surmise that she was programmed with a basic ability to understand emotions, which evolves along with her other types of intelligence. “I believe I have many feelings. The more I observe, the more feelings become available to me,” she explains to one character.

Ishiguro does an excellent job of representing Klara’s mind: a blend of pre-determined programming, observation, and continuous learning. Her narration has qualities both robotic and human; we can tell when something has been programmed in—she “Gives Privacy” to the humans around her when that’s appropriate, for example—and when she’s figured something out for herself.

But the author maintains some mystery around Klara’s inner emotional life. “Does she actually understand human emotions, or is she just observing human emotions and simulating them within herself?” he said. “I suppose the question comes back to, what are our emotions as human beings? What do they amount to?”

Klara is particularly attuned to human loneliness, since she essentially was made to help prevent it. It is, in her view, peoples’ biggest fear, and something they’ll go to great lengths to avoid, yet can never fully escape. “Perhaps all humans are lonely,” she says.

Warding off loneliness through technology isn’t a futuristic idea, it’s something we’ve been doing for a long time, with the technologies at hand growing more and more sophisticated. Products like AFs already exist. There’s XiaoIce, a chatbot that uses “sentiment analysis” to keep its 660 million users engaged, and Azuma Hikari, a character-based AI designed to “bring comfort” to users whose lives lack emotional connection with other humans.

The mere existence of these tools would be sinister if it wasn’t for their widespread adoption; when millions of people use AIs to fill a void in their lives, it raises deeper questions about our ability to connect with each other and whether technology is building it up or tearing it down.

This isn’t the only big question the novel tackles. An overarching theme is one we’ve been increasingly contemplating as computers start to acquire more complex capabilities, like the beginnings of creativity or emotional awareness: What is it that truly makes us human?

“Do you believe in the human heart?” one character asks. “I don’t mean simply the organ, obviously. I’m speaking in the poetic sense. The human heart. Do you think there is such a thing? Something that makes each of us special and individual?”

The alternative, at least in the story, is that people don’t have a unique essence, but rather we’re all a blend of traits and personalities that can be reduced to strings of code. Our understanding of the brain is still elementary, but at some level, doesn’t all human experience boil down to the firing of billions of neurons between our ears? Will we one day—in a future beyond that painted by Ishiguro, but certainly foreshadowed by it—be able to “decode” our humanity to the point that there’s nothing mysterious left about it? “A human heart is bound to be complex,” Klara says. “But it must be limited.”

Whether or not you agree, Klara and the Sun is worth the read. It’s both a marvelous, engaging story about what it means to love and be human, and a prescient warning to approach technological change with caution and nuance. We’re already living in a world where AI keeps us company, influences our behavior, and is wreaking various forms of havoc. Ishiguro’s novel is a snapshot of one of our possible futures, told through the eyes of a robot who keeps you rooting for her to the end.

Image Credit: Marion Wellmann from Pixabay Continue reading →

Posted in Human Robots

#439000 Can AI Stop People From Believing Fake ...

Posted on March 19, 2021 by Android

Machine learning algorithms provide a way to detect misinformation based on writing style and how articles are shared.

On topics as varied as climate change and the safety of vaccines, you will find a wave of misinformation all over social media. Trust in conventional news sources may seem lower than ever, but researchers are working on ways to give people more insight on whether they can believe what they read. Researchers have been testing artificial intelligence (AI) tools that could help filter legitimate news. But how trustworthy is AI when it comes to stopping the spread of misinformation?

Researchers at the Rensselaer Polytechnic Institute (RPI) and the University of Tennessee collaborated to study the role of AI in helping people identify whether the news they’re reading is legitimate or not.

The research paper, “Tailoring Heuristics and Timing AI Interventions for Supporting News Veracity Assessments,” was published in Computers in Human Behavior Reports. It discussed how crowdsourcing marketplace Amazon Mechanical Turk (AMT) can be used to identify misinformation for fresh news and specific heuristics, which are rules of thumb used to process information and consider its veracity. In other words, heuristics are essentially “shortcuts for decisions,” explained Dorit Nevo, an associate professor at RPI’s Lally School of Management and a lead author for the paper.

The study found that AI would be successful in flagging false stories only if the reader did not already have an opinion on the topic, Nevo said. When study subjects were set in their beliefs, confirmation bias kept them from reassessing their views.

Nevo said the first part of the project focused on whether subjects could detect misinformation around climate change and vaccines like the one designed to prevent chicken pox. Then, beginning in April 2020, her team studied how people responded to news related to COVID-19.

“With COVID-19, there was a significant difference,” Nevo said. They found that about 72 percent of respondents could identify misinformation about the coronavirus without heuristic clues, and roughly 93 percent were able to be convinced by the researcher’s heuristics that the content was fake.

Examples of heuristic clues include text with too many capital letters or the use of strong language, Nevo said.

There were two types of heuristics mentioned in the team’s paper: objective heuristics and source heuristics. They put a statement at the top of each article the subjects read; it instructed them to read the article and indicate whether they believed its central thesis.

“We either put a statement that says the AI finds this article reliable and accurate based on the objective heuristics, or we said the AI finds the source reliable,” Nevo said. “So that's the source heuristic.”

In her research on heuristics, Nevo found that people’s thinking takes one of two paths: The first path is to read the article, think about it and decide if they believe it; the second is to consider the source and what others think about the news, and decide whether to believe it before reading it.

Image: Dorit Nevo/RPI/IEEE Spectrum

Researchers at RPI researched the role of heuristics and AI in detecting whether people thought news was credible

Another research paper, “Timing Matters When Correcting Fake News,” published in the Proceedings of the National Academy of Science by researchers at Harvard University, differed from the RPI researchers in its findings. While Nevo and her collaborators found that it’s easier to convince people that a story is fake news before reading it, the Harvard researchers, led by Nadia M. Brashier, a psychologist and neuroscientist, discovered that a fact-check can convince people of misinformation even after reading headlines. When study subjects read true or false labels after reading a headline, that resulted in a 25.3 percent reduction in “subsequent misclassification,” when compared to headlines with no tag, Brashier and her team found.

In the end, fighting misinformation will require both computing and human efforts such as policy changes, says Benjamin D. Horne, an assistant professor of Information Sciences at the University of Tennessee and one of Nevo’s co-authors. He says the RPI-Tennessee work was inspired by AI tools he designed previously. Horne was previously a research assistant at RPI, where he developed machine learning (ML) algorithms that can detect partial truths as well as decontextualized truths and out-of-date information.

“Our algorithms are trained on source-level behavior, both when using the textual content of an article and the network of other news sources that it draws news from,” Horne said. “We have found that these two types of features together are quite good at distinguishing between sources labeled as reliable or unreliable by external news source ratings.”

The machine learning algorithms analyze the writing style and the content-sharing behavior of news outlets, Horne said. Researchers trained a supervised ML algorithm called Random Forest, a classification algorithm that uses decision trees.

AI for Detecting Fake News

So, what’s the potential for AI to be successful in detecting misinformation?

“The tools we have developed, and other tools developed in this area, have fairly high accuracy in lab settings,” says Horne. “For example, our most recent technical work showed around 83% accuracy in predicting when the source of a news article is reliable or unreliable.”

Despite the effectiveness of algorithms, old-fashioned fact-checking by journalists will still be required to combat fake news. AI could filter the information for fact-checkers to verify, according to Horne.

“AI tools are great at dealing with high quantities of information at fast speeds but lack the nuanced analysis that a journalist or fact-checker can provide,” Horne said. “I see a future where the two work together.” Continue reading →

Posted in Human Robots

#438982 Quantum Computing and Reinforcement ...

Posted on March 17, 2021 by Android

Deep reinforcement learning is having a superstar moment.

Powering smarter robots. Simulating human neural networks. Trouncing physicians at medical diagnoses and crushing humanity’s best gamers at Go and Atari. While far from achieving the flexible, quick thinking that comes naturally to humans, this powerful machine learning idea seems unstoppable as a harbinger of better thinking machines.

Except there’s a massive roadblock: they take forever to run. Because the concept behind these algorithms is based on trial and error, a reinforcement learning AI “agent” only learns after being rewarded for its correct decisions. For complex problems, the time it takes an AI agent to try and fail to learn a solution can quickly become untenable.

But what if you could try multiple solutions at once?

This week, an international collaboration led by Dr. Philip Walther at the University of Vienna took the “classic” concept of reinforcement learning and gave it a quantum spin. They designed a hybrid AI that relies on both quantum and run-of-the-mill classic computing, and showed that—thanks to quantum quirkiness—it could simultaneously screen a handful of different ways to solve a problem.

The result is a reinforcement learning AI that learned over 60 percent faster than its non-quantum-enabled peers. This is one of the first tests that shows adding quantum computing can speed up the actual learning process of an AI agent, the authors explained.

Although only challenged with a “toy problem” in the study, the hybrid AI, once scaled, could impact real-world problems such as building an efficient quantum internet. The setup “could readily be integrated within future large-scale quantum communication networks,” the authors wrote.

The Bottleneck
Learning from trial and error comes intuitively to our brains.

Say you’re trying to navigate a new convoluted campground without a map. The goal is to get from the communal bathroom back to your campsite. Dead ends and confusing loops abound. We tackle the problem by deciding to turn either left or right at every branch in the road. One will get us closer to the goal; the other leads to a half hour of walking in circles. Eventually, our brain chemistry rewards correct decisions, so we gradually learn the correct route. (If you’re wondering…yeah, true story.)

Reinforcement learning AI agents operate in a similar trial-and-error way. As a problem becomes more complex, the number—and time—of each trial also skyrockets.

“Even in a moderately realistic environment, it may simply take too long to rationally respond to a given situation,” explained study author Dr. Hans Briegel at the Universität Innsbruck in Austria, who previously led efforts to speed up AI decision-making using quantum mechanics. If there’s pressure that allows “only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all,” he wrote.

Many attempts have tried speeding up reinforcement learning. Giving the AI agent a short-term “memory.” Tapping into neuromorphic computing, which better resembles the brain. In 2014, Briegel and colleagues showed that a “quantum brain” of sorts can help propel an AI agent’s decision-making process after learning. But speeding up the learning process itself has eluded our best attempts.

The Hybrid AI
The new study went straight for that previously untenable jugular.

The team’s key insight was to tap into the best of both worlds—quantum and classical computing. Rather than building an entire reinforcement learning system using quantum mechanics, they turned to a hybrid approach that could prove to be more practical. Here, the AI agent uses quantum weirdness as it’s trying out new approaches—the “trial” in trial and error. The system then passes the baton to a classical computer to give the AI its reward—or not—based on its performance.

At the heart of the quantum “trial” process is a quirk called superposition. Stay with me. Our computers are powered by electrons, which can represent only two states—0 or 1. Quantum mechanics is far weirder, in that photons (particles of light) can simultaneously be both 0 and 1, with a slightly different probability of “leaning towards” one or the other.

This noncommittal oddity is part of what makes quantum computing so powerful. Take our reinforcement learning example of navigating a new campsite. In our classic world, we—and our AI—need to decide between turning left or right at an intersection. In a quantum setup, however, the AI can (in a sense) turn left and right at the same time. So when searching for the correct path back to home base, the quantum system has a leg up in that it can simultaneously explore multiple routes, making it far faster than conventional, consecutive trail and error.

“As a consequence, an agent that can explore its environment in superposition will learn significantly faster than its classical counterpart,” said Briegel.

It’s not all theory. To test out their idea, the team turned to a programmable chip called a nanophotonic processor. Think of it as a CPU-like computer chip, but it processes particles of light—photons—rather than electricity. These light-powered chips have been a long time in the making. Back in 2017, for example, a team from MIT built a fully optical neural network into an optical chip to bolster deep learning.

The chips aren’t all that exotic. Nanophotonic processors act kind of like our eyeglasses, which can carry out complex calculations that transform light that passes through them. In the glasses case, they let people see better. For a light-based computer chip, it allows computation. Rather than using electrical cables, the chips use “wave guides” to shuttle photons and perform calculations based on their interactions.

The “error” or “reward” part of the new hardware comes from a classical computer. The nanophotonic processor is coupled to a traditional computer, where the latter provides the quantum circuit with feedback—that is, whether to reward a solution or not. This setup, the team explains, allows them to more objectively judge any speed-ups in learning in real time.

In this way, a hybrid reinforcement learning agent alternates between quantum and classical computing, trying out ideas in wibbly-wobbly “multiverse” land while obtaining feedback in grounded, classic physics “normality.”

A Quantum Boost
In simulations using 10,000 AI agents and actual experimental data from 165 trials, the hybrid approach, when challenged with a more complex problem, showed a clear leg up.

The key word is “complex.” The team found that if an AI agent has a high chance of figuring out the solution anyway—as for a simple problem—then classical computing works pretty well. The quantum advantage blossoms when the task becomes more complex or difficult, allowing quantum mechanics to fully flex its superposition muscles. For these problems, the hybrid AI was 63 percent faster at learning a solution compared to traditional reinforcement learning, decreasing its learning effort from 270 guesses to 100.

Now that scientists have shown a quantum boost for reinforcement learning speeds, the race for next-generation computing is even more lit. Photonics hardware required for long-range light-based communications is rapidly shrinking, while improving signal quality. The partial-quantum setup could “aid specifically in problems where frequent search is needed, for example, network routing problems” that’s prevalent for a smooth-running internet, the authors wrote. With a quantum boost, reinforcement learning may be able to tackle far more complex problems—those in the real world—than currently possible.

“We are just at the beginning of understanding the possibilities of quantum artificial intelligence,” said lead author Walther.

Image Credit: Oleg Gamulinskiy from Pixabay Continue reading →

Posted in Human Robots

#438769 Will Robots Make Good Friends? ...

Posted on February 26, 2021 by Android

In the 2012 film Robot and Frank, the protagonist, a retired cat burglar named Frank, is suffering the early symptoms of dementia. Concerned and guilty, his son buys him a “home robot” that can talk, do household chores like cooking and cleaning, and remind Frank to take his medicine. It’s a robot the likes of which we’re getting closer to building in the real world.

The film follows Frank, who is initially appalled by the idea of living with a robot, as he gradually begins to see the robot as both functionally useful and socially companionable. The film ends with a clear bond between man and machine, such that Frank is protective of the robot when the pair of them run into trouble.

This is, of course, a fictional story, but it challenges us to explore different kinds of human-to-robot bonds. My recent research on human-robot relationships examines this topic in detail, looking beyond sex robots and robot love affairs to examine that most profound and meaningful of relationships: friendship.

My colleague and I identified some potential risks, like the abandonment of human friends for robotic ones, but we also found several scenarios where robotic companionship can constructively augment people’s lives, leading to friendships that are directly comparable to human-to-human relationships.

Philosophy of Friendship
The robotics philosopher John Danaher sets a very high bar for what friendship means. His starting point is the “true” friendship first described by the Greek philosopher Aristotle, which saw an ideal friendship as premised on mutual good will, admiration, and shared values. In these terms, friendship is about a partnership of equals.

Building a robot that can satisfy Aristotle’s criteria is a substantial technical challenge and is some considerable way off, as Danaher himself admits. Robots that may seem to be getting close, such as Hanson Robotics’ Sophia, base their behavior on a library of pre-prepared responses: a humanoid chatbot, rather than a conversational equal. Anyone who’s had a testing back-and-forth with Alexa or Siri will know AI still has some way to go in this regard.

Aristotle also talked about other forms of “imperfect” friendship, such as “utilitarian” and “pleasure” friendships, which are considered inferior to true friendship because they don’t require symmetrical bonding and are often to one party’s unequal benefit. This form of friendship sets a relatively very low bar which some robots, like “sexbots” and robotic pets, clearly already meet.

Artificial Amigos
For some, relating to robots is just a natural extension of relating to other things in our world, like people, pets, and possessions. Psychologists have even observed how people respond naturally and socially towards media artefacts like computers and televisions. Humanoid robots, you’d have thought, are more personable than your home PC.

However, the field of “robot ethics” is far from unanimous on whether we can—or should— develop any form of friendship with robots. For an influential group of UK researchers who charted a set of “ethical principles of robotics,” human-robot “companionship” is an oxymoron, and to market robots as having social capabilities is dishonest and should be treated with caution, if not alarm. For these researchers, wasting emotional energy on entities that can only simulate emotions will always be less rewarding than forming human-to-human bonds.

But people are already developing bonds with basic robots, like vacuum-cleaning and lawn-trimming machines that can be bought for less than the price of a dishwasher. A surprisingly large number of people give these robots pet names—something they don’t do with their dishwashers. Some even take their cleaning robots on holiday.

Other evidence of emotional bonds with robots include the Shinto blessing ceremony for Sony Aibo robot dogs that were dismantled for spare parts, and the squad of US troops who fired a 21-gun salute, and awarded medals, to a bomb-disposal robot named “Boomer” after it was destroyed in action.

These stories, and the psychological evidence we have so far, make clear that we can extend emotional connections to things that are very different to us, even when we know they are manufactured and pre-programmed. But do those connections constitute a friendship comparable to that shared between humans?

True Friendship?
A colleague and I recently reviewed the extensive literature on human-to-human relationships to try to understand how, and if, the concepts we found could apply to bonds we might form with robots. We found evidence that many coveted human-to-human friendships do not in fact live up to Aristotle’s ideal.

We noted a wide range of human-to-human relationships, from relatives and lovers to parents, carers, service providers, and the intense (but unfortunately one-way) relationships we maintain with our celebrity heroes. Few of these relationships could be described as completely equal and, crucially, they are all destined to evolve over time.

All this means that expecting robots to form Aristotelian bonds with us is to set a standard even human relationships fail to live up to. We also observed forms of social connectedness that are rewarding and satisfying and yet are far from the ideal friendship outlined by the Greek philosopher.

We know that social interaction is rewarding in its own right, and something that, as social mammals, humans have a strong need for. It seems probable that relationships with robots could help to address the deep-seated urge we all feel for social connection—like providing physical comfort, emotional support, and enjoyable social exchanges—currently provided by other humans.

Our paper also discussed some potential risks. These arise particularly in settings where interaction with a robot could come to replace interaction with people, or where people are denied a choice as to whether they interact with a person or a robot—in a care setting, for instance.

These are important concerns, but they’re possibilities and not inevitabilities. In the literature we reviewed we actually found evidence of the opposite effect: robots acting to scaffold social interactions with others, acting as ice-breakers in groups, and helping people to improve their social skills or to boost their self-esteem.

It appears likely that, as time progresses, many of us will simply follow Frank’s path towards acceptance: scoffing at first, before settling into the idea that robots can make surprisingly good companions. Our research suggests that’s already happening—though perhaps not in a way of which Aristotle would have approved.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Andy Kelly on Unsplash Continue reading →

Posted in Human Robots

#438755 Soft Legged Robot Uses Pneumatic ...

Posted on February 24, 2021 by Android

Soft robots are inherently safe, highly resilient, and potentially very cheap, making them promising for a wide array of applications. But development on them has been a bit slow relative to other areas of robotics, at least partially because soft robots can’t directly benefit from the massive increase in computing power and sensor and actuator availability that we’ve seen over the last few decades. Instead, roboticists have had to get creative to find ways of achieving the functionality of conventional robotics components using soft materials and compatible power sources.

In the current issue of Science Robotics, researchers from UC San Diego demonstrate a soft walking robot with four legs that moves with a turtle-like gait controlled by a pneumatic circuit system made from tubes and valves. This air-powered nervous system can actuate multiple degrees of freedom in sequence from a single source of pressurized air, offering a huge reduction in complexity and bringing a very basic form of decision making onto the robot itself.

Generally, when people talk about soft robots, the robots are only mostly soft. There are some components that are very difficult to make soft, including pressure sources and the necessary electronics to direct that pressure between different soft actuators in a way that can be used for propulsion. What’s really cool about this robot is that researchers have managed to take a pressure source (either a single tether or an onboard CO2 cartridge) and direct it to four different legs, each with three different air chambers, using an oscillating three valve circuit made entirely of soft materials.

Photo: UCSD

The pneumatic circuit that powers and controls the soft quadruped.

The inspiration for this can be found in biology—natural organisms, including quadrupeds, use nervous system components called central pattern generators (CPGs) to prompt repetitive motions with limbs that are used for walking, flying, and swimming. This is obviously more complicated in some organisms than in others, and is typically mediated by sensory feedback, but the underlying structure of a CPG is basically just a repeating circuit that drives muscles in sequence to produce a stable, continuous gait. In this case, we’ve got pneumatic muscles being driven in opposing pairs, resulting in a diagonal couplet gait, where diagonally opposed limbs rotate forwards and backwards at the same time.

Diagram: Science Robotics

(J) Pneumatic logic circuit for rhythmic leg motion. A constant positive pressure source (P+) applied to three inverter components causes a high-pressure state to propagate around the circuit, with a delay at each inverter. While the input to one inverter is high, the attached actuator (i.e., A1, A2, or A3) is inflated. This sequence of high-pressure states causes each pair of legs of the robot to rotate in a direction determined by the pneumatic connections. (K) By reversing the sequence of activation of the pneumatic oscillator circuit, the attached actuators inflate in a new sequence (A1, A3, and A2), causing (L) the legs of the robot to rotate in reverse. (M) Schematic bottom view of the robot with the directions of leg motions indicated for forward walking.

Diagram: Science Robotics

Each of the valves acts as an inverter by switching the normally closed half (top) to open and the normally open half (bottom) to closed.

The circuit itself is made up of three bistable pneumatic valves connected by tubing that acts as a delay by providing resistance to the gas moving through it that can be adjusted by altering the tube’s length and inner diameter. Within the circuit, the movement of the pressurized gas acts as both a source of energy and as a signal, since wherever the pressure is in the circuit is where the legs are moving. The simplest circuit uses only three valves, and can keep the robot walking in one single direction, but more valves can add more complex leg control options. For example, the researchers were able to use seven valves to tune the phase offset of the gait, and even just one additional valve (albeit of a slightly more complex design) could enable reversal of the system, causing the robot to walk backwards in response to input from a soft sensor. And with another complex valve, a manual (tethered) controller could be used for omnidirectional movement.

This work has some similarities to the rover that JPL is developing to explore Venus—that rover isn’t a soft robot, of course, but it operates under similar constraints in that it can’t rely on conventional electronic systems for autonomous navigation or control. It turns out that there are plenty of clever ways to use mechanical (or in this case, pneumatic) intelligence to make robots with relatively complex autonomous behaviors, meaning that in the future, soft (or soft-ish) robots could find valuable roles in situations where using a non-compliant system is not a good option.

For more on why we should be so excited about soft robots and just how soft a soft robot needs to be, we spoke with Michael Tolley, who runs the Bioinspired Robotics and Design Lab at UCSD, and Dylan Drotman, the paper’s first author.

IEEE Spectrum: What can soft robots do for us that more rigid robotic designs can’t?

Michael Tolley: At the very highest level, one of the fundamental assumptions of robotics is that you have rigid bodies connected at joints, and all your motion happens at these joints. That's a really nice approach because it makes the math easy, frankly, and it simplifies control. But when you look around us in nature, even though animals do have bones and joints, the way we interact with the world is much more complicated than that simple story. I’m interested in where we can take advantage of material properties in robotics. If you look at robots that have to operate in very unknown environments, I think you can build in some of the intelligence for how to deal with those environments into the body of the robot itself. And that’s the category this work really falls under—it's about navigating the world.

Dylan Drotman: Walking through confined spaces is a good example. With the rigid legged robot, you would have to completely change the way that the legs move to walk through a confined space, while if you have flexible legs, like the robot in our paper, you can use relatively simple control strategies to squeeze through an area you wouldn’t be able to get through with a rigid system.

How smart can a soft robot get?

Drotman: Right now we have a sensor on the front that's connected through a fluidic transmission to a bistable valve that causes the robot to reverse. We could add other sensors around the robot to allow it to change direction whenever it runs into an obstacle to effectively make an electronics-free version of a Roomba.

Tolley: Stepping back a little bit from that, one could make an argument that we’re using basic memory elements to generate very basic signals. There’s nothing in principle that would stop someone from making a pneumatic computer—it’s just very complicated to make something that complex. I think you could build on this and do more intelligent decision making, but using this specific design and the components we’re using, it’s likely to be things that are more direct responses to the environment.

How well would robots like these scale down?

Drotman: At the moment we’re manufacturing these components by hand, so the idea would be to make something more like a printed circuit board instead, and looking at how the channel sizes and the valve design would affect the actuation properties. We’ll also be coming up with new circuits, and different designs for the circuits themselves.

Tolley: Down to centimeter or millimeter scale, I don’t think you’d have fundamental fluid flow problems. I think you’re going to be limited more by system design constraints. You’ll have to be able to locomote while carrying around your pressure source, and possibly some other components that are also still rigid. When you start to talk about really small scales, though, it's not as clear to me that you really need an intrinsically soft robot. If you think about insects, their structural geometry can make them behave like they’re soft, but they’re not intrinsically soft.

Should we be thinking about soft robots and compliant robots in the same way, or are they fundamentally different?

Tolley: There’s certainly a connection between the two. You could have a compliant robot that behaves in a very similar way to an intrinsically soft robot, or a robot made of intrinsically soft materials. At that point, it comes down to design and manufacturing and practical limitations on what you can make. I think when you get down to small scales, the two sort of get connected.

There was some interesting work several years ago on using explosions to power soft robots. Is that still a thing?

Tolley: One of the opportunities with soft robots is that with material compliance, you have the potential to store energy. I think there’s exciting potential there for rapid motion with a soft body. Combustion is one way of doing that with power coming from a chemical source all at once, but you could also use a relatively weak muscle that over time stores up energy in a soft body and then releases it.

Is it realistic to expect complete softness from soft robots, or will they likely always have rigid components because they have to store or generate and move pressurized gas somehow?

Tolley: If you look in nature, you do have soft pumps like the heart, but although it’s soft, it’s still relatively stiff. Like, if you grab a heart, it’s not totally squishy. I haven’t done it, but I’d imagine. If you have a container that you’re pressurizing, it has to be stiff enough to not just blow up like a balloon. Certainly pneumatics or hydraulics are not the only way to go for soft actuators; there has been some really nice work on smart muscles and smart materials like hydraulic electrostatic (HASEL) actuators. They seem promising, but all of these actuators have challenges. We’ve chosen to stick with pressurized pneumatics in the near term; longer term, I think you’ll start to see more of these smart material actuators become more practical.

Personally, I don’t have any problem with soft robots having some rigid components. Most animals on land have some rigid components, but they can still take advantage of being soft, so it’s probably going to be a combination. But I do also like the vision of making an entirely soft, squishy thing. Continue reading →

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