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.


Machine Learning: The machines can translate now

by Paul Curzon, Queen Mary University of London

“The Machines can translate now…

Portion of the Rosetta Stone which has the same text written in three languages.

The stereotype of the Englishman abroad when confronted by someone who doesn’t speak English is just to say it louder. That could soon be a thing of the past as portable devices start to gain speach recognition skills and as the machines get better at translating between languages.

Traditionally machine translation has involved professional human linguists manually writing lots of translation rules for the machines to follow. Recently there have been great advances in what is known as statistical machine translation where the machine learns the translations rules automatically. It does this using a parallel corpus*: just lots of pairs of sentences; one a sentence in the original language, the other its translation. Parallel corpora* are extracted from multi-lingual news sources like the BBC web site where professional human translators have done the translations.

Let’s look at an example translation of the accompanying original arabic:

Machine Translation: Baghdad 1-1 (AFP) – The official Iraqi news agency reported that the Chinese vice-president of the Revolutionary Command Council in Iraq, Izzat Ibrahim, met today in Baghdad, chairman of the Saudi Export Development Center, Abdel Rahman al-Zamil.

Human Translation: Baghdad 1-1 (AFP) – Iraq’s official news agency reported that the Deputy Chairman of the Iraqi Revolutionary Command Council, Izzet Ibrahim, today met with Abdul Rahman al-Zamil, Managing Director of the Saudi Center for Export Development.

This example shows a sentence from an Arabic newspaper then its translation by the Queen Mary University of London’s statistical machine translator, and finally a translation by a professional human translator. The statistical translation does allow a reader to get a rough understanding of the original Arabic sentence. There are several mistakes, though.

Mistranslating the “Managing Director” of the export development center as its “chairman” is perhaps not too much of a problem. Mistranslating “Deputy Chairman” as the “Chinese vice-president” is very bad. That kind of mistranslation could easily lead to grave insults!

That reminds me of the point in ‘The Hitch-Hiker’s Guide to the Galaxy’ where Arthur Dent’s words “I seem to be having tremendous difficulty with my lifestyle,” slip down a wormhole in space-time to be overheard by the Vl’hurg commander across a conference table. Unfortunately this was understood in the Vl’hurg tongue as the most dreadful insult imaginable, resulting in them waging terrible war for centuries…

For now the human’s are still the best translators but the machines are learning from them fast!

*corpus and corpora = singular and plural for the word used to describe a collection of written texts, literally a ‘body’ of text. A corpus might be all the works written by one author, corpora might be works of several authors.


This article was originally published on the CS4FN website; there are some more articles below from CS4FN’s computer science and linguistics mini site.

Braille: binary, bits & bytes – Letters from the Victorian Smog

Letters from the Victorian Smog
by Paul Curzon, Queen Mary University of London

Reading Braille image by Myriams-Fotos from Pixabay

We take for granted that computers use binary: to represent numbers, letters, or more complicated things like music and pictures…any kind of information.That was something Ada Lovelace realised very early on. Binary wasn’t invented for computers though. Its first modern use as a way to represent letters was actually invented in the first half of the 19th century. It is still used today: Braille.

Braille is named after its inventor, Louis Braille. He was born 6 years before Ada though they probably never met as he lived in France. He was blinded as a child in an accident and invented the first version of Braille when he was only 15 in 1824 as a way for blind people to read. What he came up with was a representation for letters that a blind person could read by touch.

Choosing a representation for the job is one of the most important parts of computational thinking. It really just means deciding how information is going to be recorded. Binary gives ways of representing any kind of information that is easy for computers to process. The idea is just that you create codes to represent things made up of only two different characters: 1 and 0. For example, you might decide that the binary for the letter ‘p’ was: 01110000. For the letter ‘c’ on the other hand you might use the code, 01100011. The capital letters, ‘P’ and ‘C’ would have completely different codes again. This is a good representation for computers to use as the 1’s and 0’s can themselves be represented by high and low voltages in electrical circuits, or switches being on or off.

The first representation Louis Braille chose wasn’t great though. It had dots, dashes and blanks – a three symbol code rather than the two of binary. It was hard to tell the difference between the dots and dashes by touch, so in 1837 he changed the representation – switching to a code of dots and blanks.

He had invented the first modern form of writing based on binary.

Braille works in the same way as modern binary representations for letters. It uses collections of raised dots (1s) and no dots (0s) to represent them. Each gives a bit of information in computer science terms. To make the bits easier to touch they’re grouped into pairs. To represent all the letters of the alphabet (and more) you just need 3 pairs as that gives 64 distinct patterns. Modern Braille actually has an extra row of dots giving 256 dot/no dot combinations in the 8 positions so that many other special characters can be represented. Representing characters using 8 bits in this way is exactly the equivalent of the computer byte.

Modern computers use a standardised code, called Unicode. It gives an agreed code for referring to the characters in pretty well every language ever invented including Klingon! There is also a Unicode representation for Braille using a different code to Braille itself. It is used to allow letters to be displayed as Braille on computers! Because all computers using Unicode agree on the representations of all the different alphabets, characters and symbols they use, they can more easily work together. Agreeing the code means that it is easy to move data from one program to another.

The 1830s were an exciting time to be a computer scientist! This was around the time Charles Babbage met Ada Lovelace and they started to work together on the analytical engine. The ideas that formed the foundation of computer science must have been in the air, or at least in the Victorian smog.


Further reading

This post was first published on CS4FN and also appears on page 7 of Issue 20 of the CS4FN magazine. You can download a free PDF copy of the magazine here as well as all of our previous magazines and booklets, at our free downloads site.

The RNIB has guidance for sighted people who might be producing Braille texts for blind people, about how to use Braille on a computer and get it ready for correct printing.

This History of Braille article also references an earlier ‘Night Writing’ system developed by Charles Barbier to allow French soldiers in the 1800s to read military messages without using a lamp (which gave away their position, putting them at risk). Barbier’s system inspired Braille to create his.

A different way of representing letters is Morse Code which is a series of audible short and long sounds that was used to communicate messages very rapidly via telegraphy.

Find out about Abraham Louis Breguet’s ‘Tactful Watch‘ that let people work out what time it was by feel, instead of rudely looking at their watch while in company.


Only the fittest slogans survive!

by Paul Curzon, Queen Mary University of London

Assembly line image by OpenClipart-Vectors from Pixabay


Being creative isn’t just for the fun of it. It can be serious too. Marketing people are paid vast amounts to come up with slogans for new products, and in the political world, a good, memorable soundbite can turn the tide over who wins and loses an election. Coming up with great slogans that people will remember for years needs both a mastery of language and a creative streak too. Algorithms are now getting in on the act, and if anyone can create a program as good as the best humans, they will soon be richer than the richest marketing executive. Polona Tomašicˇ and her colleagues from the Jožef Stefan Institute in Slovenia are one group exploring the use of algorithms to create slogans. Their approach is based on the way evolution works – genetic algorithms. Only the fittest slogans survive!

A mastery of language
To generate a slogan, you give their program a short description on the slogan’s topic – a new chocolate bar perhaps. It then uses existing language databases and programs to give it the necessary understanding of language.

First, it uses a database of common grammatical links between pairs of words generated from wikipedia pages. Then skeletons of slogans are extracted from an Internet list of famous (so successful) slogans. These skeletons don’t include the actual words, just the grammatical relationships between the words. They provide general outlines that successful slogans follow.

From the passage given, the program pulls out keywords that can be used within the slogans (beans, flavour, hot, milk, …). It generates a set of fairly random slogans from those words to get started. It does this just by slotting keywords into the skeletons along with random filler words in a way that matches the grammatical links of the skeletons.

Breeding Slogans
New baby slogans are now produced by mating pairs of initial slogans (the parents). This is done by swapping bits into the baby from each parent. Both whole sections and individual words are swapped in. Mutation is allowed too. For example, adjectives are added in appropriate places. Words are also swapped for words with a related meaning. The resulting children join the new population of slogans. Grammar is corrected using a grammar checker.

Culling Slogans
Slogans are now culled. Any that are the same as existing ones go immediately. The slogans are then rated to see which are fittest. This uses simple properties like their length, the number of keywords used, and how common the words used are. More complex tests used are based on how related the meanings of the words are, and how commonly pairs of words appear together in real sentences. Together these combine to give a single score for the slogan. The best are kept to breed in the next generation, the worst are discarded (they die!), though a random selection of weaker slogans are also allowed to survive. The result is a new set of slogans that are slightly better than the previous set.

Many generations later…
The program breeds and culls slogans like this for thousands, even millions of generations, gradually improving them, until it finally chooses its best. The slogans produced are not yet world beating on their own, and vary in quality as judged by humans. For chocolate, one run came up with slogans like “The healthy banana” and “The favourite oven”, for example. It finally settled on “The HOT chocolate” which is pretty good.

Hot chocolate image by Sabrina Ripke from Pixabay

More work is needed on the program, especially its fitness function – the way it decides what is a good slogan and what isn’t. As it stands this sort of program isn’t likely to replace anyone’s marketing department. They could help with brainstorming sessions though, to spark new ideas but leaving humans to make the final choice. Supporting human creativity rather than replacing it is probably just as rewarding for the program after all.



This article was originally published on CS4FN and also appears on p13 of Creative Computing, issue 22 of the CS4FN magazine. You can download a free PDF copy of the magazine as well as all of our previous booklets and magazines from the CS4FN downloads site.



What are birds actually saying?

by Dan Stowell and Paul Curzon, Queen Mary University of London

Birds make so much noise, and it’s very complex. Is it just babble, or are they saying complicated things to each other? If so, could we work out what they are saying, what it means? Could we learn their language and speak to the birds?

Gull image by Thanasis Papazacharias from Pixabay

We know that bird communication is not as complicated as the words and sentences in human speech. So far, no one has been able to find grammatical patterns like those we find in human language. There apparently aren’t rules for birds like the ones we have about verbs and nouns. Birds don’t have to learn grammar! Exactly how complex bird languages are is still hotly debated, though.

Sometimes they’re passing on information about predators, or food, or sometimes just advertising their own fitness – showing off to get a mate (a bit like karaoke nights). Scientists have proved that such specific kinds of information are in the sounds birds make by observing bird behaviour. By playing recordings of birds and seeing how other birds react, they can see what information was communicated by a particular sound. If you play a ‘predator near’ call, for example, then other birds flee, but they stay put if you play other calls. They get the message.

Birds are definitely passing on specific information when they sing.

It turns out some birds have even learnt the languages of other animals and use it both to help those other animals and to support a life of crime. Many animals listen for the alarm calls of the animals around them, and so flee when others see a problem. Birds called Drongos, for example, act as lookouts for Meerkats, giving warning calls when they see Meerkat predators, allowing them to return to the safety of their burrows. However, the Drongos also sound false alarms every so often. They do it when they see a Meerkat with some juicy morcel. As the Meerkats run, the Drongo swoops in to steal the abandoned food.

Unfortunately for the Drongo, Meerkats are quite clever and get wise to the con. Eventually, they start to ignore the Drongo and only listen for their own Meerkat sentry’s call. The Drongo has another trick though. They are really good at mimicking sounds they hear, just like parrots. They have learnt to speak Meerkat just like the scientists do in experiments. So when the Meerkats stop reacting, the Drongos just switch tactics and start making perfect Meerkat language alarm calls instead. Once again the food is theirs.

Drongos give false alarms so they can steal food.

While most of us can’t reproduce bird sounds ourselves, and so talk directly to animals, we can certainly write programs to do it. In Star Wars, C3PO is a master of languages, speaking millions. Real robots of the near future will be able to mimic the sounds of whatever animals they wish and communicate with them in at least the simple ways that animals of different species listen and talk to each other. Perhaps something like this might be used to help protect endangered species from their predators, for example, watching for hawks and issuing timely warnings. We just have to hope they don’t turn to the Dark Side, like the Drongos, and use these skills to support a life of crime.


This article was originally published on CS4FN and in issue 21 of the CS4FN magazine ‘Computing Sounds Wild’ on p3. You can download a PDF copy of Issue 21, as well as all of our previous published material, free, at the CS4FN downloads site.

Computing Sounds Wild explores the work of scientists and engineers who are using computers to understand, identify and recreate wild sounds, especially those of birds. We see how sophisticated algorithms that allow machines to learn, can help recognize birds even when they can’t be seen, so helping conservation efforts. We see how computer models help biologists understand animal behaviour, and we look at how electronic and computer generated sounds, having changed music, are now set to change the soundscapes of films. Making electronic sounds is also a great, fun way to become a computer scientist and learn to program.

Front cover of CS4FN Issue 21 – Computing sounds wild