A sick robot dog called Max

A friend has asked me to check out her Aibo robot dog, called Max. Here he is:

Cute eh?

He's an early ERS-210 model. Charges up ok, but there's no response on switching-on. Hmm. I suspect the programme memory stick might have become corrupted. This will need a deeper examination...

FIRA 2012 Robot World Cup to be hosted by the Bristol Robotics Lab

We're all very excited because FIRA (the Federation of International Robot soccer Association), which runs an annual competition for robot soccer (and other robot sports), has awarded the 2012 event to the Bristol Robotics Lab. The 2010 event was held in Bangalore, India: check here for the web pages with 2010 results and some terrific videos. This year FIRA 2011 will be in Kaohsiung, Taiwan.

FIRA 2012 will run from 20 - 25 August 2012, just a week or so after London 2012. Alongside FIRA 2012 will be two robotics conferences: the FIRA Congress and TAROS 2012 (Towards Autonomous Robotic Systems). Here is the (under development) FIRA-TAROS 2012 web site. Here is the joint University of Bristol, UWE press release announcing the event.

The FIRA robot world cup games currently fall into 7 categories and each category is defined by the type of robot and, typically, has its own set of rules. The first six categories are all real physical robots, the 7th - SimuroSot - is all in simulation. Here's a brief summary of the 6 real robot categories with links to the full descriptions and rules on the FIRA web pages.

• HuroSot is the main category for bipedal (walking and running) humanoid robots. It is also the most comprehensive category - in addition to soccer the category includes competitions for basketball, wall climbing, weight lifting and marathon running. HuroSot robots can be up to 130cm in height, and weigh up to 30kg. We will be entering a Bristol team for HuroSot. Here are some nice videos of HuroSot competitions in 2009.
• Amiresot is a simple one-a-side soccer game for the small Amire wheeled robot, which must be fully autonomous with its own vision system. AmireSot robots play with a yellow tennis ball.
• MiroSot is the Micro Robot soccer game for wheeled robots. It's a three-a-side game (one player can be a goalkeeper), in which an external vision system tracks the position of robots - and the ball - and an external computer system computes and relays moves to the robots. Robots cannot be larger than 7.5cm x 7.5cm x 7.5cm and they play with an orange golf ball. Here is a page with a video of a 2009 MiroSot game.
• NaroSot is similar to MiroSot but with smaller wheeled robots (4cm x 4cm x 5.5cm) and is a five-a-side game. NaroSot robots play with an orange ping-pong ball.
• AndroSot is a three-a-side game for fully autonomous 'android' robots between 30 and 60cm in height. Here is a video of a 2009 AndroSot game. 
• RoboSot is a game for larger wheeled robots (20cm x 20cm x any height). It's a three-a-side game and the robots must use on-board vision, although computation may be off-board. RoboSot robots play with a yellow/green tennis ball.

Here are some of the robots entered in past competitions (from the FIRA web pages):

HuroSot

MiroSot

NaroSot

RoboSot

On Twitter and Machiavellian Intelligence

Four short months and 135 tweets ago I wrote about joining Twitter. Slightly reluctant, confused by how it worked and - if I'm completely honest - a bit sniffy about whether I could be bothered with it at all.

But just a week ago I stunned myself by realising that Twitter is now the first thing I check in the morning. Not email. After the best part of 15 years of first ritually checking my email Twitter has knocked email off the top spot(1). So what happened? What is it about Twitter that is so compelling, so addictive? Why do I love Twitter?

Actually that was just the first surprise. The second was to realise I was so pleased when my number of followers reached 50, then 60 - and last week 70.

But the thing that shocked me rigid a week ago was this. I found myself wondering how I might contrive person X (who I admired) to notice me and become my follower. What the hell was I thinking! Who's in control here - me or Twitter; do I have obsessive compulsive twitter syndrome? Do I need help - maybe go cold turkey for awhile?

But then I started thinking about it and realised that there is a very ancient instinct at work here, and Twitter is just tickling that instinct in me. Perfectly. I'm talking about Machiavellian intelligence. The kind of social intelligence that is present to some degree in all primates and well developed in Chimpanzee and monkeys such as Rhesus Macaques. So what is this kind of social intelligence? Well, if you find yourself thinking: I'm going to make friends with him and pretend I like him, but not because I want to be his friend. Oh no: he has a friend that I really want to be friends with and - through this deception - I might achieve that goal. Then you are engaged in the social politics of Machiavellian intelligence. For anyone interested in intelligence, the evolution of human intelligence, or indeed AI, Machiavellian intelligence is very interesting because it requires Theory of Mind. It was probably already well developed in the most recent common ancestor of humans and chimpanzee, around 6 million years ago.

I don't know whether it was intentional, but the very smart people who created Twitter have somehow built an ecosystem perfectly suited for this kind of game. The basic ingredients are these: firstly everyone has followers and people they follow (following). The fact that you can easily see the number of followers and following for those you follow, or who follow you, means that very quickly you establish exactly where you are in the Twitter social pecking order. These numbers mean alot to us. The alpha-tweeters are those with huge numbers of followers. They are, in the terminology of memetics, meme-founts - leaders of fashion. But even for those of us with modest circles of followers and following, the balance of numbers is significant. Our Machiavellian instinct tells us that those with a greater number of followers than following are, on balance, leaders whereas those whose following outnumbers their followers are, on balance, followers and therefore of lower Twitter status. Please understand I'm absolutely not saying they are less worthy individuals, only that this is what our Machiavellian instinct tells us in the game of Twitter.

The second ingredient that is, I think, significant is the fact that you can easily see which followers or following you have in common with someone. So it is not just a matter of numbers, it's personal. Among those you follow, and those who follow you, you really can work out very quickly who is connected to who - and the connections have social structure. If I and someone else follow each other, then we are - in a sense - equal. If, on the other hand, I see that I'm following someone else, but they don't follow me, then my Machiavellian instinct places them higher up the Twitter social scale than me. Again this may not correlate at all to real-life standing. The point I'm making is that we can't help making these Machiavellian inferences - and Twitter makes it so easy.

This brings me to the third and most brilliant ingredient: Re-tweeting. The politics of re-tweeting are fascinating and complex. Having one of your tweets re-tweeted is the equivalent of being stroked, and we love being stroked. I certainly experience a quantum of happiness(2) when one of my tweets is re-tweeted, and I'm even happier if it's re-tweeted several times. Conversely, I'm disappointed if a tweet that I thought was especially witty, insightful or apposite to current events fails to be re-tweeted. Indeed it appears to be good manners to thank those who have RT'd a tweet - which says alot of how much we value RTs. And of course to be re-tweeted by a Twitter celebrity is a precious honour, the equivalent of a favour by one of the princesses of the Twitter court.

So Twitter is powerful stuff. It's not just a micro-blogging site, it is a quite remarkable place in which we can play out to the full our ancient instinct for Machiavellian social politics.

And of course Twitter has proven itself to be a marvellous vehicle for grass-roots political activism. Is that something to do with Machiavellian intelligence too?

So now I don't feel quite so bad about my new-found Twitter addiction.

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(1) Apart from a short spell of Guardian Soulmates 3 years ago:))
(2) I propose a new unit for a quantum of happiness: the RT (re-tweet).

How Intelligent are Intelligent Robots?

When giving talks about intelligent robots I've often been faced with the question "how intelligent is your robot?" with a tone of voice that suggests "...and should we be alarmed" It's a good question but one that is extremely difficult - if not impossible - to answer properly. I usually end up giving the rather feeble answer "not very", and I might well add "perhaps about as intelligent as a lobster" (or some other species that my audience will regard as reassuringly not very smart). I'm always left with an uneasy sense that I (and robotics in general) ought to be able to give an answer to this perfectly reasonable question. (Sooner or later I'm going to get caught out when someone follows up with "and exactly how intelligent is a lobster?")

Given that the study of Artificial Intelligence is over 60 years old, and that of embodied AI (i.e. intelligent robotics) not much younger, the fact that roboticists can't properly answer the question "how intelligent are intelligent robots" is, to say the least, embarrassing. It is I believe a problem that needs some serious attention.

Let's look at the question again. There is an implied abbreviation here: what my interlocutor means is: how intelligent are intelligent robots when compared with animals and humans? What's more we all assume a kind of 'scale' of intelligence - with humans (decidedly) at the top and, furthermore, a sense that a crocodile is smarter than a lobster, and a cat smarter than a crocodile. Where, then, would we place a robot vacuum cleaner, for instance, on this scale of animal intelligence?

Ok. To answer the question we clearly need to find a single measure, or test, for intelligence that is general enough it can be applied to robots, animals or humans. It needs to have a single scale broad enough to accommodate human intelligence and simple animals. This metric - let's call it GIQ for General (non-species-specific) Intelligence Quotient - would need to be extensible downwards - to accommodate single celled organisms (or plants for that matter) and of course robots because they're not very smart. Thinking ahead it should also be extensible upwards for super-human AI (which we keep being told is only a few decades away). Does such a measure exist already? No, I don't think it does, but I did come across this news posting on physorg.com a few days ago with the promising opening line How do you use a scientific method to measure the intelligence of a human being, an animal, a machine or an extra-terrestrial? It refers to a paper titled Measuring Universal Intelligence: Towards an Anytime Intelligence Test. I haven't been able to read the paper (it is behind a paywall) but - even from the abstract - it's pretty clear the problem isn't solved. In any event I'm doubtful because the news writeup talks of "interactive exercises in settings with a difficulty level estimated by calculating the so-called Kolmogorov complexity", which suggests a test that the agent being tested has to engage in. Well that's not going to work if you're testing the intelligence of a spider is it?

So let's set aside the problem of comparing the intelligence of robots with animals (or ET) for a moment. Are there existing non-species specific intelligence measures? This interesting essay by Jonathan Ball: The question of animal intelligence outlines several existing measures based on neural physiology. In summary they include:

• Encephalization Quotient (EQ): which measures whether the brain of a given species is bigger or smaller than would be expected, compared with that of other animals its size (winner: Humans)
• Cortical Folding: a measure based on the degree of cortical folding (winner: Dolphins)
• Connectivity: a measure based on comparing the average number of connections per neuron (winner: Humans)

Interestingly, if we take the connectivity measure - which Jonathan Ball suggests offers the greatest degree of correlation with intelligence - then if our robot is controlled by an artificial neural network we might actually have a common basis for comparison of human and robot intelligence.

So, even if none of them are entirely satisfactory it's clear that there has been a great deal of work on measures of animal intelligence. What about the field of robotics - are there intelligence metrics for comparing one robot with another (say a vacuum cleaning robot with a toy robot dog)? As far as I'm aware the answer is a resounding no. (Of course the same is not true in the field of AI where passing the Turing Test has become the iconic - if controversial - holy grail.)

But all of this presupposes, firstly, that we can agree on what we mean by 'intelligence' - which we do not. And secondly, that intelligence is a single thing that any one animal, or robot, can have more or less of* - which is also very doubtful.

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*An observation made by an anonymous reviewer of one of my papers, for which I am very grateful.

New experiments in embodied evolutionary swarm robotics

My PhD student Paul has started a new series of experiments in embodied evolution in the swarm robotics lab. Here's a picture showing his experiment with 3 Linux e-puck robots in a small circular arena together with an infra-red beacon (at about 2 o'clock).



The task the robots are engaged in collective foraging for food. Actually there's nothing much to see here because the food items are virtual (i.e. invisible) blobs in the arena that the robots have to 'find', then 'pick up' and 'transport' to the nest (again virtually). The nest region is marked by the infra-red beacon - so the robots 'deposit' the food items in the pool of IR light in the arena just in front of the beacon. The reason we don't bother making physical food items and grippers, etc, is that this would entail engineering work that's really not important here. You see, here we are not so interested in collective foraging - it's just a test problem for investigating the thing we're really interested in, which is embodied evolution.

The point of the experiment is this: at the start the robots don't know how to forage for food; during the experiment they must collectively 'evolve' the ability to forage. Paul is here researching the process of collective evolution. Before explaining what's going on 'under the hood' of these robots, let me give some background. Evolutionary robotics has been around for nearly 20 years. The idea is that instead of hand-designing the robot's control system we use an artificial process inspired by Darwinian evolution, called a genetic algorithm. It's really a way of automating the design. Evolutionary algorithms have been shown to be a very efficient way of searching the so-called design space and, in theory, will come up with (literally evolve) better solutions than we can invent by hand. Much more recent is the study of evolutionary swarm robotics (which is why there's no Wikipedia entry yet), which tackles the harder problem of evolving the controllers for individual robots in a swarm such that, collectively, the swarm will self-organise to solve the overall task.

Still with me? Good. Now let me explain what's going on in the robots of Paul's experiment. Each robot has inside it a simulation of itself and it's environment (food, other robots and the nest). That simulation is not run once, but many times over within a genetic algorithm inside the robot. Thus each robot is running a simulation of the process of evolution, of itself, in itself. When that process completes (about once a minute), the best performing evolved controller is transferred into the real robot's controller. Since the embodied evolutionary process runs through several (simulated) generations of robot controller, then the final winner of each evolutionary competition is, in effect, a great great... grandchild of the robot controller at the start of each cycle. While the real robots are driving around in the arena foraging (virtual) food and returning it to the nest, simulated evolution is running - in parallel - as a background process. Every minute or so the real robot's controllers are updated with the latest generation of (hopefully improved) evolved controllers so what we observe is the robots gradually getting better and better at collective foraging. If you think this sounds complicated – it is. The software architecture that Paul has built to accomplish this is ferociously complex and all the more remarkable because it fits within a robot about the size of a salt shaker. But in essence it is like this: what’s going on inside the robots is a bit like you imagining lots of different ways of riding a bike over and over, inside your head, while actually riding a bike.

Putting a simulation inside a robot is something roboticists refer to as ‘robots with internal models’ and if we are to build real-world robots that are more autonomous, more adaptable – in short smarter, this kind of complexity is something we will have to master.

If you’ve made it this far, you might well ask the question, “what if the simulation inside the robot is an inaccurate representation of the real world – won’t that mean the evolved controller will be rubbish?” You would be right. One of the problems that has dogged evolutionary robotics is known as the 'reality gap'. It is the gap between the real world and the simulated world, which means that a controller evolved (and therefore optimised) in simulation typically doesn't work very well - or sometimes not at all - when transferred to the real robot and run in the real world. Paul is addressing this hard problem by also evolving the embedded simulators at the same time as evolving the robot's controllers; a process called co-evolution. This is where having a swarm of robots is a real advantage: just as we have a population of simulated controllers evolving inside each robot, we have a population of simulators - one per robot - evolving collectively across the swarm.

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Related blog posts:
Environment-driven distributed evolutionary adaptation
Walterian creatures

Open Science: from good intentions to hesitant reality

At the start of the Artificial Culture project we made a commitment to an Open Science approach. Actually translating those good intentions into reality has proven much more difficult than I had expected. But now we've made a start, and interestingly the open science part of this research project is turning into a project within a project.

So what's the story? Well, firstly we didn't really know what we meant by open science. We were, at the start, motivated by two factors. One, a strong sense that open science is a Good Thing. And, second, a rather more pragmatic idea that the project might be helped through having a pool of citizen scientists who would help us with interpretation of the results. We knew that we would generate a lot of data and also believed we would benefit from fresh eyes looking over that data, uncoloured - as we are - by the weight of hypotheses and high expectations. We thought we could achieve this simply by putting the whole project, live - as it happens - on the web.

Sounds simple: put the whole project on the web. And now that I put it like this, hopelessly naive. Especially given that we had not budgeted for the work this entails. So, this became a DIY activity fitted into spare moments using free Web tools, in particular Google Sites.

We started experimental work, in earnest, in March 2010 - about two and a half years into the project (building the robots and experimental infrastructure took about two years). Then, by July 2010 I started to give some thought to uploading the experimental data to the project web. But it took me until late October to actually make it happen. Why? Well it took a surprising amount of effort to figure out the best way of structuring and organising the experiments, and the data sets from those experiments, together with the structure of the web pages on which to present that data. But then even when I'd decided on these things I found myself curiously reluctant to actually upload the data sets. I'm still not sure why that was. It's not as if I was uploading anything important, like Wikileaks posts. Perhaps it's because I'm worried that someone will look at the data and declare that it's all trivial, or obvious. Now this may sound ridiculous but posting the data felt a bit like baring the soul. But maybe not so ridiculous given the emotional and intellectual investment I have in this project.

But, having crossed that hurdle, we've made a start. There are more data sets to be loaded (the easy part), and a good deal more narrative to be added (which takes a deal of effort). The narrative is of course critical because without it the data sets are just meaningless numbers. To be useful at all we need to explain (starting at the lowest level of detail):

1. what each of the data fields in each of the data files in each data set means;
2. the purpose of each experimental run: number of robots, initial conditions, algorithms, etc;
3. the overall context for the experiments, including the methodology and the hypotheses we are trying to test.

I said at the start of this blog post that the open science has become a project within a project and happily this aspect is now receiving the attention it deserves: yesterday project co-investigator Frances Griffiths spent the day in the lab here in Bristol, supported by Ann Grand (whose doctoral project is on the subject of Open Science and Public Engagement).

Will anyone be interested in looking inside our data, and - better still - will we realise our citizen science aspirations? Who knows. Would I be disappointed if no-one ever looks at the data? No, actually not. The openness of open science is its own virtue. And we will publish our findings confident that if anyone wants to look at the data behind the claims or conclusions in our papers they can.

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Postscript: See also Frances Griffiths' blog post Open Science and the Artificial Culture Project

On optimal foraging, cod larvae and robot vacuum cleaners

On Monday I took part in a meeting of the Complex Systems Dynamics (CoSyDy) network in Warwick. The theme of the meeting was Movement in models of mathematical biology, and I heard amazing talks about (modelling) albatross flight patterns, e-coli locomotion, locust swarming and the spread of epidemics. (My contribution was about modelling an artificial system - a robot swarm.) Although a good deal of the maths was beyond me, I was struck by a common theme of our talks that I'll try and articulate in this blog post.

The best place to start is by (badly) paraphrasing a part of Jon Pitchford's brilliant description of optimal foraging strategies for cod larvae. Cod larvae, he explained, feed on patches of plankton. They are also very small and if the sea is turbulent the larvae have no chance of swimming in any given direction (i.e. toward a food patch), so the best course of action is to stop swimming and go where the currents take you. Of course the food patches also get washed around by the current so the odds are good that the food will come to you anyway. There's no point wasting energy chasing a food patch. Only if the sea is calm is it worthwhile for the cod larvae to swim toward a food patch. Thus, swim (toward food) when the sea is calm, but don't swim when it's rough, is the optimal foraging strategy for the cod larvae.

It occurred to me that there's possibly a direct parallel with robot vacuum cleaners, like the Roomba.  A robot vacuum cleaner is also foraging, not for food of course, but dirt in the carpet. For the robot vacuum cleaner the equivalent of a rough, turbulent, sea is a room with chaotically positioned furniture. The robot doesn't need a fancy strategy for covering the floor: it just drives ahead and every time it drives up to a wall or piece of furniture it stops to avoid a collision, makes a random turn and drives off again in a straight line. This is the robot's best strategy for reasonable coverage (and hence cleaning) of the floor in a chaotic environment (i.e. a normal room). Only if the room was relatively large and empty (i.e. a calm sea) would the robot (like the cod larvae) need a more sophisticated strategy for optimal cleaning - such as moving in a pattern across the whole area to try and find all the dirt.

Robot vacuum cleaners, like cod larvae, can exploit the chaos in their environment and hence get away with simple (i.e. stupid) foraging strategies. I can't help wondering - given the apparently unpredictable current economic environment - if there's really no point governments or individuals trying to invent sophisticated economic strategies. Perhaps the optimal response to economic turbulence is the KISS principle.

Why large robot swarms (and maybe also multi-cellular life) need immune systems.

Just gave our talk at DARS 2010, basically challenging the common assumption that swarm robot systems are highly scalable by default. In other words the assumption that if the system works with 10 robots, it will work just as well with 10,000. As I said this morning "sorry guys, that assumption is seriously incorrect. Swarms with as few as 100 robots will almost certainly not work unless we invent an active immune system for the swarm". The problem is that the likelihood that some robots partially fail - in other words fail in such a way as to actually hinder the overall swarm behaviour - quickly increases with swarm size. The only way to deal with this - and hence build large swarms - will be to invent a mechanism that enables good robots to both identify and disable partially failed robots. In other words an immune system.

Actually - and this is the thing I really want to write about here - I think this work hints toward an answer to the question "why do animals need immune systems?". I think it's hugely interesting that evolution had to invent immune systems very early in the history of multi-cellular life. I think the basic reason for this might be the very same reason - outlined above - that we can't scale up from small to huge (or even moderately large) robot swarms without something that looks very much like an immune system. Just like robots, cells can experience partial failures: not enough failure to die, but enough to behave badly - badly enough perhaps to be dangerous to neighbouring cells and the whole organism. If the likelihood of one cell failing in this bad way is constant, then it's self-evident that its much more likely that some will fail in this way in an organism with 10,000 cells than 10 cells. And with 10 million cells (still a small number for animals) it becomes a certainty.

Here is the poster version of our paper.

New video of 20 evolving e-pucks

In June I blogged about Nicolas Bredeche and Jean-Marc Montanier working with us in the lab to transfer their environment-driven distributed evolutionary adaptation algorithms to real robots, using our Linux extended e-pucks. Nicolas and Jean-Marc made another visit in August to extend the experiments to a larger swarm size, of 20 robots; they made a YouTube movie and here it is:



In the narrative on YouTube Nicolas writes

This video shows a fully autonomous artificial evolution within a population of ~20 completely autonomous real (e-puck) robots. Each robot is driven by its "genome" and genomes are spread whenever robots are close enough (range: 25cm). The most "efficient" genomes end up being those that successfully drive robots to meet with each other while avoiding getting stuck in a corner.

There is no human-defined pressure on robot behavior. There is no human-defined objective to perform.

The environment alone puts pressure upon which genomes will survive (ie. the better the spread, the higher the survival rate). Then again, the ability for a genome to encode an efficient behavioral strategy first results from pure chance, then from environmental pressure.

In this video, you can observe how going towards the sun naturally emerges as a good strategy to meet/mate with other (it is used as a convenient "compass") and how changing the sun location affect robots behavior.

Note: the 'sun' is the static e-puck with a white band around it.

Twitter

Well, I can't believe I'm on Twitter: https://twitter.com/alan_winfield

Not at all sure I understand what I'm doing yet. There' some puzzling terminology to learn - what's retweeting for instance..? (It sounds like a word from The Meaning of Liff.)

The reason I joined is because I wanted to respond to the questions on @scienceexchange. The first question is

Given the rate of discovery of exo-planets - is there still any doubt that we are not alone in the universe?

And my twittered answer:

Depends: life maybe a little more probable; intelligent life still highly improbable, see Drake's equation

I like the challenge of trying to construct a useful answer in 140 characters.

Google robot car: Great but proving the AI is safe is the real challenge

Great to read that Google are putting some funding into driverless car technology with the very laudable aims of reducing robot traffic fatalities and reducing carbon emissions. Google have clearly assembled a seriously talented group led by Stanford's Sebastian Thrun. (One can only imagine the Boardroom discussions in the car manufacturers this week on Google's entry into their space.)

While this is all very good, I think it's important to keep the news in perspective. Driverless cars have been in development for a long time and what Sebastian has announced this weekend is not a game changing leap forward. To be fair his blog post's main claim is the record for distance driven but Joe Wuensche's group at University BW Munich has a remarkable record of driverless car research; fifteen years ago their Mercedes 500 drove from Munich to Denmark on regular roads, at up to 180 km/h, with surprisingly little manual driver intervention (about 5%). I've seen MuCAR-3, the latest autonomous car from Joe's group, in action in the European Land Robotics Challenge and it is deeply impressive - navigating its way through forest tracks with no white lines or roadside kerbs to help the car's AI figure out where the road's edges are.

So the technology is pretty much there. Or is it?

The problem is that what Thrun's team at Google, and Wuensche's team at UBM, have compellingly demonstrated is proof of principle: trials under controlled conditions with a safety driver present (somewhat controversially at ELROB, because the rules didn't allow a safety driver). That's a long way from your granny getting into her car which then autonomously drives her to the shops without her having to pay attention in case she needs to hit the brakes when the car decides to take a short cut across the vicar's lawn. The fundamental unsolved problem is how to prove the safety and dependability of the Artificial Intelligence (AI) driving the car. This is a serious problem not just for driverless cars, but all next-generation autonomous robots. Proving the safety of a system, i.e. proving that it will both always do the right thing and never do the wrong thing, is very hard right now for conventional systems that have no learning in them (i.e. no AI). But with AI the problem gets a whole lot worse: the AI in the Google car, to quote "becomes familiar with the environment and its characteristics", i.e. it learns. And we don't yet know how to prove the correctness of systems that learn.

In my view that is the real challenge.

Can robots be Three Laws safe?

I'm with about 25 people in a hotel in the New Forest to talk about the ethical, legal and societal issues around robotics. We are a diverse crew: a core of robotics and AI folk, richly complemented by academics in psychology, law, ethics, philosophy, culture, performance and art history. This joint EPSRC/AHRC workshop was an outcome of a discussion on robot ethics at the EPSRC Societal Issues Panel in November 2009. (See also my post The Ethical Roboticist.)

Of course in any discussion about robot ethics it is inevitable that Asimov's Three Laws of Robotics will come up and, I must admit, I've always insisted that they have no value whatsoever. They were, after all, a fictional device for creating stories with dramatic moral ambiguities - not a serious attempt to draw up a moral code of robots. Today I've been forced to revise that opinion. Amazingly we have succeeded in drafting a new set of five 'laws', not for robots themselves but for designers and operators of robots. (You can't have laws for robots because they are not persons - or at least not for the foreseeable future.)

I can't post them here just yet - a joint statement needs to be drafted and agreed first. But to answer the question in the title of this post - no, robots can't be Three Laws Safe, but they quite possibly could be Five Laws Compliant.

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Postscript: here is a much better description of the workshop on Lilian Edwards' excellent blog.

Robot imitation as a method for modelling the foundations of social life

Robot imitation as a method for modelling the foundations of social life: a meeting of robotics and sociology to explore the spread of behaviours through mimesis

Here is the video, posted earlier this month by Frances Griffiths on YouTube, of the meeting of robotics and sociology I blogged about on 21st June. No need for me to write anything more - Roger Stotesbury's excellent 10 minute film explains the whole thing...

Morphogenetic Engineering at ANTS

I'm at the excellent Swarm Intelligence conference in Brussels, called appropriately ANTS. This morning there is a special session on morphogenetic engineering, chaired by René Doursat, of the complex systems institute in Paris. Morphogenetic engineering is the name coined for a new cross over between biology and engineering. Current engineered systems are designed and 'built'. Biological systems on the other hand grow from seeds or embryos. Morphogenetic engineering asks the question, might it be possible to 'grow' complex engineered systems, like robots?

Of course with current materials: metal and plastic, we can't grow robots so many of the ideas of morphogenetic engineering remain, for the time being, future concepts. But I think we'll see some exciting developments in this new sub-field as new materials become available.

Here is an image from our talk* on autonomous distributed morphogenesis in the Symbrion project, presented during the special session. Here you see robots being recruited to join the 2D planar organism during its formation.



* Wenguo Liu and Alan FT Winfield, 'Autonomous morphogenesis in self-assembling robots using IR-based sensing and local communications', ANTS 2010.

Darn - conference paper soundly rejected

As someone who believes in - and from time-to-time advocates - the Open Science approach, I need to practise what I preach. That means being open about the things that don't go according to plan in a research project - including when papers that you think are really great get rejected following peer review. So, let me 'fess up. A paper I submitted to the highly regarded conference Distributed Autonomous Robotic Systems, describing results from the Artificial Culture project, has just been soundly rejected by the reviewers.

Of course, having papers rejected is not unusual. And, like most academics, I tend to react with indignation ("how dare they"), dismissal ("the reviewers clearly didn't understand the work") and embarrassment (hangs head in shame). After a day or two the first two feelings subside, but the embarrassment remains. None of us likes it when our essays come back marked C-. That is why this blog post is not especially comfortable to write.

My paper had four anonymous reviews, and each one was thorough and thoughtful. And - although not all reviewers recommended rejection - the overall verdict to reject was, in truth, fully justified. The paper, titled A Multi-robot Laboratory for Experiments in Embodied Memetic Evolution failed to either fully describe the laboratory, or the experiments. Like most conference papers there was a page limit (12 pages) and I tried to fit too much into the paper.

So, what next for this paper? Well the work will not be wasted. We shall revise the paper - taking account of the reviewers comments - and submit it elsewhere. So, despite my embarrassment, I am grateful to those reviews (I don't know who you are but if you should read this blog - thank you!).

And for Open Science. Well, a fully paid-up card carrying Open Scientist would publish here the original paper and the reviews. But it seems to me improper to publish the reviews without first getting the reviewers' permission - and I can't do that because I don't know who they are. And I shouldn't post the paper either, since to do so would compromise our ability to submit the same work (following revision) somewhere else. So Open Science, even with the best of intentions, has its hands tied by publications protocols.

On The Human on Temes: an emerging third replicator

Several weeks ago I was contacted by On The Human, a forum for researchers across science and the humanities to share ideas, and asked if I would like to take part in an online debate in response to an essay by Susan Blackmore. The forum runs one of these debates every two weeks and there are some pretty interesting writers and debates (which - it seems - are moderated and time limited).

So, I looked out for Sue's essay, which appeared yesterday 23rd August. I thought about it (actually had a head start because we had debated temes during a memelab meeting) and posted my response this morning. Here is Sue's essay Temes: An Emerging Third Replicator, the collected comments, and Sue's responses to those comments.

Open-hardware Linux e-puck extension board published

It's now over two years since I first blogged about our Linux-enhanced e-puck, designed by my colleague Dr Wenguo Liu. Since then, the design has gone through several improvements and is now very stable and reliable. We've installed the board on all 50 of our e-puck robots and it has also been adopted for use in swarm robotics projects by Jenny Owen at York, Andy Guest at Abertay Dundee and Newport.

Since the e-puck robot is open-hardware, Wenguo and I were keen that our extension board should follow the same principle, and so the complete design has been published online at sourceforge here http://lpuck.sourceforge.net/. All of the hardware designs, together with code images and an excellent installation manual written by Jean-Charles Antonioli are here.


















Here's a picture of the extension board. The big chip is an ARM9 microcontroller and the small board hanging off some wires is the WiFi card (in fact it's a WiFi USB stick with the plastic casing removed).

And here is a picture of one of our e-pucks with the Linux extension board fitted, just above the red skirt. The WiFi card is now invisible because it is fitted neatly into a special slot on the underside of the yellow 'hat'.

The main function of the yellow hat is the matrix of pins on the top, that we use for the reflective spheres needed by our Vicon tracking system to track the exact position of each robot during experiments. You can see one of the spheres very strongly reflecting the camera flash in this photo. The function of the red skirt is so that robots can see each other, with their onboard cameras. You can see the camera in the small hole in the middle of the red skirt. Without the red skirt the robots simply don't see each other too well, at least partly because of their transparent bodies.

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postscript (added Feb 2011): Here's the reference to our paper describing the extension board:
Liu W, Winfield AFT, 'Open-hardware e-puck Linux extension board for experimental swarm robotics research', Microprocessors and Microsystems, 35 (1), 2011, doi:10.1016/j.micpro.2010.08.002.

Open-ended Memetic Evolution, or is it?

Just finished a paper describing some new results on open-ended memetic evolution from the Artificial Culture project. I describe in some detail one particular experiment in which 2 robots imitate each others' movements. However, here the robots don't simply imitate the last thing they saw; instead they learn and save every observed movement sequence, then when it's a robot's turn to dance it selects one of its 'learned' dances, from memory, at random.

Here is a plot of the movements of the 2 robots for one particular experiment; this picture has been generated by a tool developed by Wenguo Liu that allows us to 'play back' the tracking data recorded by the Vicon position tracking system. The visualisation tool changes the colour of each 'dance', which makes it much easier to then analyse what's going on during the experiment.


Epuck 9 (on the left) starts by making a 3 sided 'triangle' dance, numbered 1 above. Epuck 12 (on the right) then imitates this - badly - as meme number 2, which is a kind of figure-of-8 pattern. It is interesting to see that this 4-sided figure-of-8 movement pattern then appears to become dominant, perhaps because of the initially poor fidelity imitation (1 → 2), then the high fidelity imitation of 2 by epuck9 (2 → 3), then the re-enaction of meme 2 as meme 4. And then subsequent copies of the same figure-of-8 meme then appear to be reasonably good copies, which reinforces the dominance of that meme.

Since the robots are selecting which observed and learned meme to enact, at random, then there is no 'direction' to the meme evolution here. Memes can get longer or shorter - both in the number of sides to the movement pattern, and the length of those sides, and the resulting patterns arise in an unpredictable way from the imperfect 'embodied' imitation of the robots. Thus, we appear to have demonstrated here, open-ended memetic evolution.

Here is a screen captured low-resolution (sorry) movie of the sequence:

Warwick Mimesis project visit to the lab

As a follow-up to a talk I gave last December in Warwick, we were visited in the lab today by a group of social and complexity scientists from Warwick including Frances Griffiths, Steve Fuller and Nick Lee. We had a hugely interesting day discussing the extent to which (or, indeed, if at all) robots could be used to model mimesis in society.

The day started with me describing the embodied imitation-of-movement experiments that we are currently doing here within the Artificial Culture project, and demonstrating the latest version of the Copybots experiment. After lunch we then had a round table discussion about whether or not such a simple model might have value in social science research and - somewhat to my surprise - there seemed to be strong consensus that there is value and that this (radical) new approach to embodied modelling is something we should actively pursue in future joint projects.

The meeting was filmed by Roger Stotesbury of Jump Off The Screen and I hope to post a link to the video record of the meeting on this blog.

Postscript: here is my blog post with Roger's film of the meeting.

Walking with Robots wins Academy Award

No, not that academy, but an academy award all the same. Last night WWR won the Royal Academy of Engineering 2010 Rooke medal for the Public Promotion of Engineering. What can I say. It was wonderful for Walking with Robots to be recognised and acknowledged in this way. It was a great project. If there had been an acceptance speech we would have had a large number of thankyous: the EPSRC who funded WWR; the amazing WWR network of roboticists and engagers from about 12 universities and as many companies; Claire Rocks who - as brilliant WWR network coordinator - more than anyone made things happen, and of course the RAEng for this award. Thank you! And we had a wonderful evening.

Here we are receiving the award from Lord Browne (third from the left). On the left is Noel Sharkey and Owen Holland, and on the right me, Karen Bultitude and Claire Rocks.