The Ultimate (do nothing) machine

by Jo Brodie, Queen Mary University of London

A black box with an on-off switch at ON. The top flips open and a robotivc finger pokes out to push the switch back to OFF.
This ultimate machine is a commercially produced version of Minsky’s idea. Image by Drpixie from Wikimedia CC-BY-SA-4.0

In 1952 computer scientist and playful inventor, Marvin Minsky, designed a machine which did one thing, and one thing only. It switched itself off. It was just a box with a motor, switch and something to flip (toggle) the switch off again after someone turned it on. Science fiction writer Arthur C. Clarke thought there was something ‘unspeakably sinister’ about a machine that exists just to switch itself off and hobbyist makers continue to create their own variations today.

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This article was funded by UKRI, through Professor Ursula Martin’s grant EP/K040251/2 and grant EP/W033615/1.

An ode to technology

by Paul Curzon, Queen Mary University of London

Cunning contraptions date back to ancient civilisations.

A female statue staring with head turned
Image by Devanath from Pixabay

People have always been fascinated by automata: robot-style contraptions allowing inanimate animal and human figures to move, long before computers could take the place of a brain.

Records show they were created in ancient Egypt, China, and Greece. In the renaissance Leonardo designed them for entertainment, and more recently magicians have bedazzled audiences with them.

The island of Rhodes was a centre for mechanical engineering in Ancient Greek times, and the Greeks were great inventors who loved automata. According to an Ode by Pindar the island was covered with automata:

The animated figures stand.

Adorning every public street.

And seem to breathe in stone,

Or move their marble feet.


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Cunning Computational Contraptions

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This article was funded by UKRI, through Professor Ursula Martin’s grant EP/K040251/2 and grant EP/W033615/1.

Swallow a slug-bot to catch a …

by Paul Curzon, Queen Mary University of London

Imagine swallowing a slug (hint not only a yucky thought but also not a good idea as it could kill you)…now imagine swallowing a slug-bot … also yucky but in the future it might save your life.

When people accidentally swallow solid objects that won’t pass through their digestive system, or are toxic, it can be a big problem. Once an object passes beyond your stomach it becomes hard to get at.

That is where the slug shaped robot comes in. The idea of scientists at the Chinese University of Hong Kong is that a robot like a slug could crawl down your throat to retrieve whatever you had swallowed.

If you think of robots as solid, hard things then that would be the last thing you might want to swallow (aside from an actual slug), and certainly not to catch the previous solid thing you swallowed. You may be right. However, that is where the soft slug-shaped robot comes in.

It is easy to make or buy slime-like “silly” putty. Add iron filings to slime putty and you can make it stretch and sway and even move around with magnets yourself. You can buy such magnetic slime at science museums…it is fun to play with though you definitely shouldn’t swallow it.

The scientists have taken that general idea though and using special materials created a similar highly controllable bot that can be moved around using a magnet-based control system. It is made of a special material that is magnetic and slime-like but coated in silicon dioxide to stop it being poisonous.

They have shown that they can control it to squeeze through narrow gaps and encircle small objects, carrying them away with it…essentially what would be needed to recover objects that have been swallowed.

It needs a lot more work to make sure it is safe to really be swallowed. Also to be a real autonomous robot it would need to have sensors included somehow, and be connected to some sort of intelligent system to automatically control its behaviour. However, with more research that all may become possible.

So in the future if you don’t fancy swallowing a slug-bot, you’d better be far more careful about what else you swallow first. Of course, if it turns out slug like robots can break down, so get stuck themselves, you may then be in a position of needing to swallow a bird-bot to catch the slug-bot. How absurd …

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The cs4fn blog is funded by EPSRC, through grant EP/W033615/1.

How to get a head in robotics (includes a free papercraft activity with a robot that expresses ’emotions’)

by Paul Curzon, Queen Mary University of London

EMYS robot

If humans are ever to get to like and live with robots we need to understand each other. One of the ways that people let others know how they are feeling is through the expressions on their faces. A smile or a frown on someone’s face tells us something about how they are feeling and how they are likely to react. Some scientists think it might be possible for robots to express feelings this way too, but understanding how a robot can usefully express its ‘emotions’ (what its internal computer program is processing and planning to do next), is still in its infancy. A group of researchers in Poland, at Wroclaw University of Technology, have come up with a clever new design for a robot head that could help a computer show its feelings. It’s inspired by the Teenage Mutant Ninja Turtles cartoon and movie series.

The real Teenage Mutant Ninja Turtle
Their turtle-inspired robotic head called EMYS, which stands for EMotive headY System is cleverly also the name of a European pond turtle, Emys orbicularis. Taking his inspiration from cartoons, the project’s principal ‘head’ designer Jan Kedzierski created a mechanical marvel that can convey a whole range of different emotions by tilting a pair of movable discs, one of which contains highly flexible eyes and eyebrows.

The real Emys orbicularis (European pond turtle)

Eye see
The lower disc imitates the movements of the human lower jaw, while the upper disk can mimic raising the eyebrows and wrinkling the forehead. There are eyelids and eyebrows linked to each eye. Have a look at your face in the mirror, then try pulling some expressions like sadness and anger. In particular look at what these do to your eyes. In the robot, as in humans, the eyelids can move to cover the eye. This helps in the expression of emotions like sadness or anger, as your mirror experiment probably showed.

Pop eye
But then things get freaky and fun. Following the best traditions of cartoons, when EMYS is ‘surprised’ the robot’s eyes can shoot out to a distance of more than 10 centimetres! This well-known ‘eyes out on stalks’ cartoon technique, which deliberately over-exaggerates how people’s eyes widen and stare when they are startled, is something we instinctively understand even though our eyes don’t really do this. It makes use of the fact that cartoons take the real world to extremes, and audiences understand and are entertained by this sort of comical exaggeration. In fact it’s been shown that people are faster at recognising cartoons of people than recognising the un- exaggerated original.

High tech head builder
The mechanical internals of EMYS consist of lightweight aluminium, while the covering external elements, such as the eyes and discs, are made of lightweight plastic using 3D rapid prototyping technology. This technology allows a design on the computer to be ‘printed’ in plastic in three dimensions. The design in the computer is first converted into a stack of thin slices. Each slice of the design, from the bottom up, individually oozes out of a printer and on to the slice underneath, so layer-by-layer the design in the computer becomes a plastic reality, ready for use.

Facing the future
A ‘gesture generator’ computer program controls the way the head behaves. Expressions like ‘sad’ and ‘surprised’ are broken down into a series of simple commands to the high-speed motors, moving the various lightweight parts of the face. In this way EMYS can behave in an amazingly fluid way – its eyes can ‘blink’, its neck can turn to follow a person’s face or look around. EMYS can even shake or nod its head. EMYS is being used on the Polish group’s social robot FLASH (FLexible Autonomous Social Helper) and also with other robot bodies as part of the LIREC project ( [archived]). This big project explores the question of how robot companions could interact with humans, and helps find ways for robots to usefully show their ‘emotions’.

Do try this at home
In this issue, there is a chance for you to program an EMYS-like robot. Follow the instructions on the Emotion Machine in the centre of the magazine (see printable version below) and build your own EMYS. By selecting a series of different commands in the Emotion Engine boxes, the expression on EMYS’s face will change. How many different expressions can you create? What are the instructions you need to send to the face for a particular expression? What emotion do you think that expression looks like – how would you name it? What would you expect the robot to be ‘feeling’ if it pulled that face?

Print, cut out and make your own emotional robot. The strips of paper at the top (‘sliders’) containing the expressions and letters are slotted into the grooves on the robot’s face and happy or annoyed faces can created by moving the sliders.

Go further
Why not draw your own sliders, with different eye shapes, mouth shapes and so on. Explore and experiment! That’s what computer scientists do.


This article was originally published on CS4FN (Computer Science For Fun) and on page 7 of issue 13 of the CS4FN magazine. You can download a free PDF copy of that issue, as well as all of our other free magazines and booklets.


Standup Robots

‘How do robots eat pizza?’… ‘One byte at a time’. Computational Humour is real, but it’s not jokes about computers, it’s computers telling their own jokes.

Robot performing
Image from istockphoto

Computers can create art, stories, slogans and even magic tricks. But can computers perform themselves? Can robots invent their own jokes? Can they tell jokes?

Combining Artificial Intelligence, computational linguistics and humour studies (yes you can study how to be funny!) a team of Scottish researchers made an early attempt at computerised standup comedy! They came up with Standup (System to Augment Non Speakers Dialogue Using Puns): a program that generates riddles for kids with language difficulties. Standup has a dictionary and joke-building mechanism, but does not perform, it just creates the jokes. You will have to judge for yourself as to whether the puns are funny. You can download the software from here. What makes a pun funny? It is a about the word having two meanings at exactly the same time in a sentence. It is also about generating an expectation that you then break: a key idea about what is at the core of creativity too.

A research team at Virginia Tech in the US created a system that started to learn about funny pictures. Having defined a ‘funniness score’ they created a computational model for humorous scenes, and trained it to predict funniness, perhaps with an eye to spotting pics for social media posting, or not.

But are there funny robots out there? Yes! RoboThespian programmed by researchers at Queen Mary University of London, and Data, created by researchers at Carnegie Mellon University are both robots programmed to do stand-up comedy. Data has a bank of jokes and responds to audience reaction. His developers don’t actually know what he will do when he performs, as he is learning all the time. At his first public gig, he got the crowd laughing, but his timing was poor. You can see his performance online, in a TED Talk.

RoboThespian did a gig at the London Barbican alongside human comedians. The performance was a live experiment to understand whether the robot could ‘work the audience’ as well as a human comedian. They found that even relatively small changes in the timing of delivery make a big difference to audience response.

What have these all got in common? Artificial Intelligence, machine learning and studies to understand what humour actually is, are being combined to make something that is funny. Comedy is perhaps the pinnacle of creativity. It’s certainly not easy for a human to write even one joke, so think how hard it is distill that skill into algorithms and train a computer to create loads of them.

You have to laugh!

Watch RoboThespian [EXTERNAL]

– Jane Waite, Queen Mary University of London, Summer 2017

Download Issue 22 of the cs4fn magazine “Creative Computing” here

Lots more computing jokes on our Teaching London Computing site

Sabine Hauert: Swarm Engineer

by Paul Curzon, Queen Mary University of London

Based on a 2016 talk by Sabine Hauert at the Royal Society

Sabine Hauert is a swarm engineer. She is fascinated by the idea of making use of swarms of robots. Watch a flock of birds and you see that they have both complex and beautiful behaviours. It helps them avoid predators very effectively, for example, so much so that many animals behave in a similar way. Predators struggle to fix on any one bird in all the chaotic swirling. Sabine’s team at the University of Bristol are exploring how we can solve our own engineering problems: from providing communication networks in a disaster zone to helping treat cancer, all based on the behaviours of swarms of animals.

A murmuration  - a flock of starlings

Sabine realised that flocks of birds have properties that are really interesting to an engineer. Their ability to scale is one. It is often easy to come up with solutions to problems that work in a small ‘toy’ system, but when you want to use it for real, the size of the problem defeats you. With a flock, birds just keep arriving, and the flock keeps working, getting bigger and bigger. It is common to see thousands of Starlings behaving like this – around Brighton Pier most winter evenings, for example. Flocks can even be of millions of birds all swooping and swirling together, never colliding, always staying as a flock. It is an engineering solution that scales up to massive problems. If you can build a system to work like a flock, you will have a similar ability to scale.

Flocks of birds are also very robust. If one bird falls out of the sky, perhaps because it is caught by a predator, the flock itself doesn’t fail, it continues as if nothing happened. Compare that to most systems humans create. Remove one component from a car engine and it’s likely that you won’t be going anywhere. This kind of robustness from failure is often really important.

Swarms are an example of emergent behaviour. If you look at just one bird you can’t tell how the flock works as a whole. In fact, each is just following very simple rules. Each bird just tracks the positions of a few nearest neighbours using that information to make simple decisions about how to move. That is enough for the whole complex behaviour of the flock to emerge. Despite all that fast and furious movement, the birds never crash into each other. Fascinated, Sabine started to explore how swarms of robots might be used to solve problems for people.

Her first idea was to create swarms of flying robots to work as a communications network, providing wi-fi coverage in places it would otherwise be hard to set up a network. This might be a good solution in a disaster area, for example, where there is no other infrastructure, but communication is vital. You want it to scale over the whole disaster area quickly and easily, and it has to be robust. She set about creating a system to achieve this.

The robots she designed were very simple, fixed wing, propellor-powered model planes. Each had a compass so it knew which direction it was pointing and was able to talk to those nearest using wi-fi signals. It could also tell who its nearest neighbours were. The trick was to work out how to design the behaviour of one bird so that appropriate swarming behaviour emerged. At any time each had to decide how much to turn to avoid crashing into another but to maintain the flock, and coverage. You could try to work out the best rules by hand. Instead, Sabine turned to machine learning.

“Throwing those flying robots

and seeing them flock

was truly magical”

The idea of machine learning is that instead of trying to devise algorithms that solve problems yourself, you write an algorithm for how to learn. The program then learns for itself by trial and error the best solution. Sabine created a simple first program for her robots that gave them fairly random behaviour. The machine learning program then used a process modelled on evolution to gradually improve. After all evolution worked for animals! The way this is done is that variations on the initial behaviour are trialled in simulators and only the most successful are kept. Further random changes are made to those and the new versions trialled again. This is continued over thousands of generations, each generation getting that little bit better at flocking until eventually a behaviour of individual robots results that leads to them swarming together.

Sabine has now moved on to to thinking about a situation where swarms of trillions of individuals are needed: nanomedicine. She wants to create nanobots that are each smaller than the width of a strand of hair and can be injected into cancer patients. Once inside the body they will search out and stick themselves to tumour cells. The tumour cells gobble them up, at which point they deliver drugs directly inside the rogue cell. How do you make them behave in a way that gives the best cancer treatment though? For example, how do you stop them all just sticking to the same outer cancer cells? One way might be to give them a simple swarm behaviour that allows them to go to different depths and only then switch on their stickiness, allowing them to destroy all the cancer cells. This is the sort of thing Sabine’s team are experimenting with.

Swarm engineering has all sorts of other practical applications, and while Sabine is leading the way, some time soon we may need lots more swarm engineers, able to design swarm systems to solve specific problems. Might that be you?

Explore swarm behaviour using the Oxford Turtle system [EXTERNAL] (click the play button top centre) to see how to run a flocking simulation as well as program your own swarms.

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Punk robots learn to pogo

It’s the second of three punk gigs in a row for Neurotic and the PVCs, and tonight they’re sounding good. The audience seem to be enjoying it too. All around the room the people are clapping and cheering, and in the middle of the mosh pit the three robots are dancing. They’re jumping up and down in the style of the classic punk pogo, and they’ve been doing it all night whenever they like the music most. Since Neurotic came on the robots can hardly keep still. In fact Neurotic and the PVCs might be the best, most perfect band for these three robots to listen to, since their frontman, Fiddian, made sure they learned to like the same music he does.

Programming punks

It’s a tough task to get a robot to learn what punk music sounds like, but there are lots of hints lurking in our own brains. Inside your brain are billions of connected cells called neurons that can send messages to one another. When and where the messages get sent depends on how strong each connection is, and we forge new connections whenever we learn something.

What the robots’ programmers did was to wire up a network of computerised connections like the ones in a real brain. Then they let the robots sample lots of different kinds of music and told them what it was, like reggae, pop, and of course, Fiddian’s collection of classic punk. That way the connections in the neural network got stronger and stronger – the more music the robots listened to, the easier it got for them to recognise what kind of stuff it was. When they recognised a style they’d been told to look out for, they would dance, firing a cylinder of compressed air to make them jump up and down.

The robots’ first gig

The last step was to tell the robots to go out and enjoy some punk. The programmers turned off the robots’ neural connections to other kinds of music, so no Kylie or Bob Marley would satisfy them. They would only dance to the angry, churning sound of punk guitars. The robots got dressed up in spray-painted leather, studded belts and safety pins, so with their bloblike bodies they looked like extra-tough boxing gloves on sticks. Then the three two-metre tall troublemakers went to their first gig.

Whenever a band begins to play, the robots’ computer system analyses the sound coming from the stage. If the patterns in it look the same as the idea of punk music they’ve learned, the robots begin to dance. If the pattern isn’t quite right, they stand still. For lots of songs they hardly dance at all, which might seem weird since all the bands that are playing the gig call themselves punk bands. Except there are many different styles of punk music, and the robots have been brought up listening to Fiddian’s favourites. The other styles aren’t close enough to the robots’ idea of punk – they’ve developed taste, and it’s the same as Fiddian’s. Which is why the robots go crazy for Neurotic and the PVCs. Fiddian’s songs are influenced by classic punk like the Clash, the Sex Pistols and Siouxsie & the Banshees, which is exactly the music he’s taught the robots to love. As the robots jump wildly up and down, it’s clear that Neurotic and the PVCs now have three tall, tough, computerised superfans.