Paris conference on Safe and Ethical AI

Earlier this month I was privileged to part of the inaugural conference of the International Association for Safe and Ethical AI. The two day conference was held in Paris, on February 6^th and 7^th, and hosted by the OECD.  The timing and location of the conference was arranged to directly precede the governmental AI summit on February 10^th and 11^th. I was one of around 650 invited representatives from academia, civil society, industry, media, and government. It was a remarkable meeting, with terrific keynote talks, including three from Nobel prize winners, Geoffrey Hinton, Maria Ressa and Joseph Stiglitz.  

As someone who has been worrying about robot and AI ethics for longer than most who attended I was *very* pleased that there was a strong consensus around the need for regulation, supported by standards, alongside urgent concerns over the huge energy and water costs of AI that are completely at odds with sustainable development goals.

The conference concluded by publishing a Call to Action for lawmakers, academics, and the public ahead of the AI Summit, with ten critical action items. Overall, the action items are very good. I’m especially pleased to see ‘mandatory reporting of incidents’ in action 5. This is something I lobbied for. My one disappointment however is that the call for action statement has no explicit mention of the need to mitigate the energy costs of AI.

Here below are a few photos from the conference

A slide from Joseph Stiglitz’ wonderful keynote: AI and Economic Risk: Assessment and Mitigation.

 

A slide from Kate Crawford’s excellent keynote: Hyperscaled: The Global Challenge of Sustainability in AI.

  

Me with Oxford colleague Pericle Salvini presenting our work on accident investigation.

New paper: A Simulated real-world upper-body Exoskeleton Accident and Investigation

Back in February I posted a very brief account of our third RoboTIPS simulated accident and investigation, centred on an upper-body exoskeletion in an industrial setting. Since then we've published a paper with a full account. My colleague Pericle Salvini presented the paper at the 9th International Conference on Robot Ethics and Standards (ICRES 2024), last week.

Here is the paper abstract:

This paper describes the enactment of a simulated (mock) accident involving an upper-body exoskeleton and its investigation. The accident scenario is enacted by role-playing volunteers, one of whom is wearing the exoskeleton. Following the mock accident, investigators – also volunteers – interview both the subject of the accident and relevant witnesses. The investigators then consider the witness testimony alongside robot data logged by the ethical black box, in order to address the three key questions: what happened?, why did it happen?, and how can we make changes to prevent the accident happening again? This simulated accident scenario is one of a series we have run as part of the RoboTIPS project, with the overall aim of developing and testing both processes and technologies to support social robot accident investigation.

 The paper sets out, for the first time, the experimental method we have developed:

1. The accident scenario is enacted by human volunteers, role playing the subject of the accident, together with both direct  and indirect witnesses. The subject is the person to whom the accident happens. Direct witnesses are those who either witness or discover the accident, and indirect witnesses are those who might be supervisors or managers of the subject and/or the facility, or representatives of the robot's manufacturer. 
2. Prior to the enactment the project team brief the volunteers. Each briefing is specific to the role and, with the exception of the subject, volunteers are briefed only on their role, and not the whole scenario. This is so that they witness the accident (or it's aftermath) for the first time during the enactment. Only the subject is fully briefed on the scenario, including the safety aspects explained below, so that they are confident that they will not come to harm or be fearful during the enactment.
3. The enactment is stage managed by project team members. Although the simulation resembles a piece of theatre, volunteers are not asked to learn any lines. Apart from any specific action essential to the scenario (which will be prompted by the stage manager) the volunteers are invited to ad lib in a way that is appropriate to the roles they are playing. Volunteers are asked to wait in a side room until they are called a few moments before they are needed.
4. Safety of the volunteers, and especially the subject, is of paramount importance. Thus, if the scenario simulates physical harm to the subject, then – when the accident happens – the enactment is briefly suspended by the stage manager and the subject is helped into the position they might be expected to be in, following the accident. The project team conduct a safety risk assessment and if necessary modify the scenario and/or its stage management to mitigate any risks and the simulation is only undertaken after university research ethics approval.
5. The accident investigators are also volunteers and, ideally, the lead accident investigator has expertise and/or experience in accident investigation. Robotics expertise is not essential, as the aims and process of investigation are common to all accident or incident (near miss) investigations. The accident investigators are not briefed on the scenario, only the type of robot involved. Necessarily the accident investigators are not present during the enactment of the simulated accident. To reduce the time burden on all volunteers we stage the accident and its investigation on a single day, with the accident investigators arriving after the enactment. 

 

 

 

 

 

 

 

A simulated upper body exoskeleton accident and investigation

On Wednesday 21 February we ran the third of our RoboTIPS simulated accident scenarios in the Bristol Robotics Lab. This scenario focussed on an upper-body exoskeleton in an industrial environment.



Above left we see Dan working to move boxes, with the physical support of the wonderful Tribonix exoskeleton. On the right Dan has fallen to the floor, attended by his manager Monica and paramedic Ben. The simulation was carefully scripted and stage managed to ensure that none of the volunteers were hurt or, indeed, ever at risk.

 

Following the simulation the accident was investigated by lead investigator Carl and co-investigator Jack. Carl Macrae is a leading authority on accident investigation. Here we see Jack and Carl interviewing expert witness Appolinaire, observed by RoboTIPS project lead Marina Jirotka.

In addition to witness testimony our investigators were also able to examine Ethical Black Box data logs collected from the exoskeleton during the simulated accident.

The simulated accident scenario was a huge success. The various roles (not all of which are shown in the photos here) were acted brilliantly by our volunteers Dan Read, Ashwin Chandapur, Monica Monica, Surin Machaiah, Ben Allen and Dr Appolinaire Etoundi. And despite a complicated scenario which included human-human as well as human-robot interaction, our accident investigators Prof Carl Macrae and Jack Hughes were able to deduce, with reasonable accuracy, what happened and why. We are especially grateful to Romain Derval and Filip Hanus, co-founders of Tribonix, for both kindly agreeing to the use of their exoskeleton and generously working with RoboTIPS during the planning and enactment of this simulation.

The simulation was subject to Research Ethics Committee approval CATE-2324-218.

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See also: 

Our first mock social robot accident and investigation

Robot Accident Investigation 

A Draft Open Standard for an Ethical Black Box

About 5 years ago we proposed that all robots should be fitted with the robot equivalent of an aircraft Flight Data Recorder to continuously record sensor and relevant internal status data. We call this an ethical black box (EBB). We argued that an ethical black box will play a key role in the processes of discovering why and how a robot caused an accident, and thus an essential part of establishing accountability and responsibility.

Since then, within the RoboTIPS project, we have developed and tested several model EBBs, including one for an e-puck robot that I wrote about in this blog, and another for the MIRO robot. With some experience under our belts, we have now drafted an Open Standard for the EBB for social robots - initially as a paper submitted to the International Conference on Robots Ethics and Standards. Let me now explain first why we need a standard, and second why it should be an open standard.

Why do we need a standard specification for an EBB? As we outline in our new paper, there are four reasons:

1. A standard approach to EBB implementation in social robots will greatly benefit accident and incident (near miss) investigations. 
2. An EBB will provide social robot designers and operators with data on robot use that can support both debugging and functional improvements to the robot. 
3. An EBB can be used to support robot ‘explainability’ functions to allow, for instance, the robot to answer ‘Why did you just do that?’ questions from its user. And,
4. a standard allows EBB implementations to be readily shared and adapted for different robots and, we hope, encourage manufacturers to develop and market general purpose robot EBBs.

And why should it be an Open Standard? Bruce Perens, author of The Open Source Definition, outlines a number of criteria an open standard must satisfy, including:

• “Availability: Open standards are available for all to read and implement.
  
• Maximize End-User Choice: Open Standards create a fair, competitive market for implementations of the standard.
• No Royalty: Open standards are free for all to implement, with no royalty or fee.
• No Discrimination: Open standards and the organizations that administer them do not favor one implementor over another for any reason other than the technical standards compliance of a vendor’s implementation.
• Extension or Subset: Implementations of open standards may be extended, or offered in subset form.”

These are *good* reasons.

The most famous and undoubtedly the most impactful Open Standards are those that specified Internet protocols, such as FTP and email. They were, and still are, called Requests for Comments (RFCs) to reflect the fact that they were - especially in the early years - drafts for revision. As a mark of respect we also regard our draft 0.1 Open Standard for an EBB for Social Robots, as an RFC. You can find draft 0.1 in Annex A of the paper on arXiv here.

Not only is this a first draft, it is also incomplete, covering only the specification of the data and its format, that should be saved in an EBB for social robots. Given that the EBB data specification is at the heart of the EBB standard, we feel that this is sufficient to be opened up for comments and feedback. We will continue to extend the specification, with subsequent versions also published on arXiv.

Please feel free to either submit comments to this blog post (best because everyone can see the comments), or by contacting me directly via email. All constructive comments that result in revisions to the standard will be acknowledged in the standard.

Our first mock social robot accident and investigation

Robot accidents are inevitable. These days the likelihood of serious accidents involving industrial robots is pretty low (but not zero), because such robots are generally inside safety cages. But a newer generation of social robots - robots designed to interact directly with people, including vulnerable elderly people or children - means that accidents are now much more likely. And if we also take into account ethical harms alongside physical harms, then the potential for accidents increases still further. Psychological harms include addiction, over trusting, or deception, and societal harms include privacy violations. For more on these ethical harms see my blog post outlining an ethical risk assessment of a smart robot teddy bear.

It has puzzled me for some years that there has been almost no research on robot accident investigation. In the RoboTIPS project we are addressing this deficit by developing both the technology - which we call an Ethical Black Box (EBB) - and the processes of robot accident investigation. One of the most exciting aspects of RoboTIPS is that we're running a series of mock, i.e. staged, social robot accidents in order to road test the EBB and investigation processes in as close to a real situation as is feasible in a research project. RoboTIPS started in March 2019, but then just as we were ready to trial our first mock accident the Covid pandemic hit, and closed down the lab.

So it was great that last week we finally managed to run the a pilot of our first (of three) mock accident scenarios. The scenario, based around an assisted living robot helping an elderly person to live independently, was sketched out in late 2019, and then - during the lockdown - rehearsed in a number of online events, including a podcast radio play for Oxford Sparks and CSI Robot during the UKRAS Festival of Robotics 2021.

Here is the scenario:

Imagine that your elderly mother, or grandmother, has an assisted living robot to help her live independently at home. The robot is capable of fetching her drinks, reminding her to take her medicine and keeping in touch with family. Then one afternoon you get a call from a neighbour who has called round and sees your grandmother collapsed on the floor. When the paramedics arrive they find the robot wandering around apparently aimlessly. One of its functions is to call for help if your grandmother stops moving, but it seems that the robot failed to do this

To enact this scenario we needed a number of volunteers: one to act as Rose - the subject of the accident, a second as the neighbour who discovers the accident and raises the alarm, a third as the paramedic who attends to Rose, a fourth who acts in the role of the cleaner and a fifth in the role of manager of the group of homes in which Rose lives. We also needed volunteers to act as members of the accident investigation team who are called in to try and discover what happened, why it happened and, if possible, what changes need to be made to how to ensure the accident doesn't happen again.

This is the mock accident taking place in the kitchen of our assisted living studio. Left shows the neighbour, acted by Paul, discovering Ross, acted by Alex, injured on the floor. (Note the chair on its side.) Right is the paramedic, role-played by Luc, attending to Ross. Meanwhile the Pepper robot is moving around somewhat aimlessly.

Our brilliant Research Fellow Dr Anouk van Maris, who organised the whole setup, persuaded five colleagues from the Bristol Robotics Lab. All were male, so Rose became Ross. Only one volunteer: Alex, who played the part of Ross, was fully briefed. The other four role played brilliantly and, although they were briefed on their roles, they were not told what was going to happened to Ross, or the part the Pepper robot played (or maybe didn't play) in the accident. Two colleagues from Oxford, Lars and Keri, kindly volunteered to act as the accident investigators. Lars and Keri also had no prior knowledge of the circumstances of the accident, and had to rely on (i) inspecting the robot and the scene of the accident, (ii) the data from the robot's EBB, and (iii) testimonies from Ross, the neighbour, the paramedic, the cleaner and the facility manager.

Here we see Lars interviewing Medhi, who acted as the house manager, while Ben, acting as the cleaner, waits to be interviewed. Inside the studio Keri is interviewing the neighbour and parademic.

So, what were the findings of our accident investigators? They did very well indeed. Close examination of the EBB data, alongside consideration of the (not always reliable) witness testimony enabled Lars and Keri to correctly deduce the role that the robot played in the accident. They were also able to make several recommendations on operational changes.  But I will not reveal their findings in detail here as we intend to run the same mock accident again soon with a different set of volunteers and - in case any of them should read this blog - I don't want to give the game away!

Acknowledgements

Very special thanks to Dr Anouk van Maris. Also Dr Pericle Salvini, who worked with Anouk in finalising the detail of the scenario and during the pilot itself. Also, huge thanks to BRL volunteers Dr Alex Smith, Dr Paul Bremner, Dr Luc Wijnen, Mehdi Sobhani and Dr Ben Ward-Cherrier. And last but not least a very big thank you to Dr Lars Kunze, Oxford Robotics Institute and Keri Grieman, Dept of Computer Science, Oxford.

From the left: Pericle, Ben, Lars, Alex, Keri, Medhi, Paul, Anouk, Luc, Lola and me. Pepper is looking nervously at Lola.

Ethics is the new Quality

This morning I took part in the first panel at the BSI conference The Digital World: Artificial Intelligence.  The subject of the panel was AI Governance and Ethics. My co-panelist was Emma Carmel, and we were expertly chaired by Katherine Holden.

Emma and I each gave short opening presentations prior to the Q&A. The title of my talk was Why is Ethical Governance in AI so hard? Something I've thought about alot in recent months.

Here are the slides exploring that question.

 

And here is what I said.

Early in 2018 I wrote a short blog post with the title Ethical Governance: what is it and who's doing it? Good ethical governance is important because in order for people to have confidence in their AI they need to know that it has been developed responsibly. I concluded my piece by asking for examples of good ethical governance. I had several replies, but none were nominating AI companies.

So. why is it that 3 years on we see some of the largest AI companies on the planet shooting themselves in the foot, ethically speaking? I’m not at all sure I can offer an answer but, in the next few minutes, I would like to explore the question: why is ethical governance in AI so hard? 

But from a new perspective. 

Slide 2

In the early 1970s I spent a few months labouring in a machine shop. The shop was chaotic and disorganised. It stank of machine oil and cigarette smoke, and the air was heavy with the coolant spray used to keep the lathe bits cool. It was dirty and dangerous, with piles of metal swarf cluttering the walkways. There seemed to be a minor injury every day.

Skip forward 40 years and machine shops look very different. 

Slide 3

So what happened? Those of you old enough will recall that while British design was world class – think of the British Leyland Mini, or the Jaguar XJ6 – our manufacturing fell far short. "By the mid 1970s British cars were shunned in Europe because of bad workmanship, unreliability, poor delivery dates and difficulties with spares. Japanese car manufacturers had been selling cars here since the mid 60s but it was in the 1970s that they began to make real headway. Japanese cars lacked the style and heritage of the average British car. What they did have was superb build quality and reliability" [1].

What happened was Total Quality Management. The order and cleanliness of modern machine shops like this one is a strong reflection of TQM practices. 

Slide 4

In the late 1970s manufacturing companies in the UK learned - many the hard way - that ‘quality’ is not something that can be introduced by appointing a quality inspector. Quality is not something that can be hired in.

This word cloud reflects the influence from Japan. The words Japan, Japanese and Kaizen – which roughly translates as continuous improvement – appear here. In TQM everyone shares the responsibility for quality. People at all levels of an organization participate in kaizen, from the CEO to assembly line workers and janitorial staff. Importantly suggestions from anyone, no matter who, are valued and taken equally seriously.

Slide 5

In 2018 my colleague Marina Jirotka and I published a paper on ethical governance in robotics and AI. In that paper we proposed 5 pillars of good ethical governance. The top four are:

• have an ethical code of conduct, 
• train everyone on ethics and responsible innovation,
• practice responsible innovation, and
• publish transparency reports.

The 5th pillar underpins these four and is perhaps the hardest: really believe in ethics.

Now a couple of months ago I looked again at these 5 pillars and realised that they parallel good practice in Total Quality Management: something I became very familiar with when I founded and ran a company in the mid 1980s [2].

Slide 6 

So, if we replace ethics with quality management, we see a set of key processes which exactly parallel our 5 pillars of good ethical governance, including the underpinning pillar: believe in total quality management.

I believe that good ethical governance needs the kind of corporate paradigm shift that was forced on UK manufacturing industry in the 1970s.

Slide 7

In a nutshell I think ethics is the new quality

Yes, setting up an ethics board or appointing an AI ethics officer can help, but on their own these are not enough. Like Quality, everyone needs to understand and contribute to ethics. Those contributions should be encouraged, valued and acted upon. Nobody should be fired for calling out unethical practices.

Until corporate AI understands this we will, I think, struggle to find companies that practice good ethical governance [3]. 

Quality cannot be ‘inspected in’, and nor can ethics.

Thank you.

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Notes.

[1]    I'm quoting here from the excellent history of British Leyland by Ian Nicholls. 

[2]    My company did a huge amount of work for Motorola and - as a subcontractor - we became certified software suppliers within their six sigma quality management programme.

[3]    It was competitive pressure that forced manufacturing companies in the 1970s to up their game by embracing TQM. Depressingly the biggest AI companies face no such competitive pressures, which is why regulation is both necessary and inevitable.

The Grim Reality of Jobs in Robotics and AI

The reality is that AI is in fact generating a large number of jobs already. That is the good news. The bad news is that they are mostly - to put it bluntly - crap jobs. 

There are several categories of such jobs. 

At the benign end of the spectrum is the work of annotating images, i.e. looking at images and identifying features then labelling them. This is AI tagging. This work is simple and incredibly dull but important because it generates training data sets for machine learning systems. Those systems could be AIs for autonomous vehicles and the images are identifying bicycles, traffic lights etc. The jobs are low-skill low-pay and a huge international industry has grown up to allow the high tech companies to outsource this work to what have been called white collar sweatshops in China or developing countries. 

A more skilled version of this kind of job are translators who are required to ‘assist’ natural language translation systems who get stuck on a particular phrase or word.

And there is another category of such jobs that are positively dangerous: content moderators. These are again outsourced by companies like Facebook, to contractors who employ people to filter abusive, violent or illegal content. This can mean watching video clips and making a decision on whether the clip is acceptable or not (and apparently the rules are complex), over and over again, all day. Not surprisingly content moderators suffer terrible psychological trauma, and often leave the job burned out after a year or two. Publicly Facebook tells us this is important work, yet content moderators are paid a fraction of what staffers working on the company campus earn. So not that important.

But jobs created by AI and automation can also be physically dangerous. The problem with real robots, in warehouses for instance, is that like AIs they are not yet good enough to do everything in the (for the sake of argument) Amazon warehouse. So humans have to do the parts of the workflow that robots cannot yet do and - as we know from press reports - these humans are required to work super fast and behave, in fact, as if they are robots. And perhaps the most dehumanizing part of the job for such workers is that, like the content moderators (and for that matter Uber drivers or Deliveroo riders), their workflows are managed by algorithms, not humans.

We roboticists used to justifiably claim that robots would do jobs that are too dull, dirty and dangerous for humans. It is now clear that working as human assistants to robots and AIs in the 21st century is dull, and both physically and/or psychologically dangerous. One of the foundational promises of robotics has been broken. This makes me sad, and very angry.

The text above is a lightly edited version of my response to the Parliamentary Office of Science and Technology (POST) request for comments on a draft horizon scanning article. The final piece How technology is accelerating changes in the way we work was published a few weeks ago.

The Energy Cost of Online Living in Lockdown

Readers of his blog will know that one of the many things ethical I worry about is the energy cost of AI. As part of the work I'm doing with Claudia Pagliari and her National Expert Group on Digital Ethics for Scotland I've been looking also into the energy costs of what is - for many of us - everyday digital life in lockdown. I don't yet have a complete set of results but what I have found so far is surprising - and not in a good way.

So far I've looked into the energy costs of (i) uploading to the cloud, (ii) streaming video (i.e. from iPlayer or Netflix), and (iii) video conferencing.

(i) Uploading to the cloud. This 2017 article in the Stanford Magazine explains that when you save a 1 Gbyte file – that’s about 1 hour of video - to your laptop’s disk drive the energy cost is 0.000005 kWh, or 5 milliWatt hours. Save the same file to the Cloud and the energy cost is between 3 and 7 kWh. For comparison your electric kettle burns about 3 kWh. This mean that the energy cost of saving to the cloud is about a million times higher than to your local disk drive. 

The huge difference makes sense when you consider that there is a very complex international network of switches, routers and exchange hubs, plus countless amplifiers maintaining signal strength over long distance transmission lines. All of this consumes energy. Then add a slice of the energy costs of the server farm.

(ii) Streaming video. This article in The Times from May 2019 makes the claim that streaming a 2 hour HD movie from Netflix incurs the same energy cost as boiling 10 kettles (based on the sustainable computing research of Mike Hazas). To estimate  how much energy that equates to we need to guess how full the kettle is. A half full 3kWh kettle will take about 2 minutes to boil, and consume therefore 100 Watts. Do that 10 times and you've burned 1kW. A DVD player typically consumes 8 Watts, so streaming costs 125 times more energy.

Again this makes sense against uploading to the cloud, except that here you are downloading from Netflix servers. A 2 hour HD movie is alot of data, around 10GBytes, so 10 times more than the case for (i) above.

(iii) Video conferencing. This post on David Mytton's excellent blog explores the energy cost of Zoom meetings in some detail. David estimates that a 1 hour video zoom call with 6 participants generates between 5 and 15GB of data and that the data transfer consumes between 0.07 – 0.22kWh of electricity. Using our benchmark of kettles boiled this is pretty modest - at most less than one tenth of the energy cost. 

However this estimate makes 2 assumptions: first that you are connected via cable or fixed line - which here in the UK costs 0.015kWh per GByte. A mobile connection costs about seven times that at 0.1kWh/GB. And second, this estimate measures only the energy costs of data transmission and fails to take account of the energy costs of Zoom's data centres, which - if (i) and (ii) here are anything to go by, could be significant, especially since there aren't any in the UK and the default servers are in the US.

As this article on the Zoom blog explains, Zoom calls are not peer to peer. The video from each participant is streamed first to a zoom server then broadcast to every other person on the call. As David Mytton says Zoom don't release information on the overall energy costs of calls. I strongly suspect that if server energy costs were factored in they would be in line with cases (i) and (ii) above. Even so, I feel sure that David Mytton's overall conclusion remains true: that the energy cost of Zoom meetings is significantly lower than all but local or regional travel.

 

I would like to see networking services like cloud storage, video on demand and video conferencing publish a meaningful energy cost. When we buy packaged food from the supermarket we expect to read the calorific energy value of each item, broken down into fat, salt and so on.  It would be great if every online transaction, from sending an email, to watching a movie revealed its energy/carbon cost. Not just for energy geeks like me, but to remind all of us that the Digital Economy is *very* energy hungry.

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I would welcome any additional data which either adds to the above (especially the energy costs for smaller online transactions like tweets, emails or card payments), or shows that the estimates above are wrong. 

Related blog posts:

On Sustainable Robotics
Energy and Exploitation: AIs dirty secrets
What's wrong with Consumer Electronics? 

 

On Sustainable Robotics

The climate emergency brooks no compromise: every human activity or artefact is either part of the solution or it is part of the problem. 

I've worried about the sustainability of consumer electronics for some time, and, more recently, the shocking energy costs of big AI. But the climate emergency has also caused me to think hard about the sustainability of robots. In recent papers we have defined responsible robotics as

... the application of Responsible Innovation in the design, manufacture, operation, repair and end-of-life recycling of robots, that seeks the most benefit to society and the least harm to the environment.

I will wager that few robotics manufacturers - even the most responsible - pay much attention to repairability and environmental impact. And, I'm ashamed to say, very little robotics research is focused on the development of sustainable robots. A search on google scholar throws up just a handful of great papers detailing work on upcycled and sustainable robots (2018), sustainable robotics for smart cities (2018), and sustainable soft robots (2020).

I was then delighted when, a few weeks ago, my friend and colleague Michael Fisher, drafted a proposal for a new standard on Sustainable Robotics. The proposal received strong support from the BSI robotics committee. Here is the formal notice requesting comments on Michael's proposal: BS XXXX Guide to the Sustainable Design and Application of Robotic Systems. Anyone can comment (although you do need to register first). The deadline is 1 April 2021. 

So what would make a robot sustainable? In my view it would have to be:

1. Made from sustainable materials. This means the robot should, as far as possible, use recycled materials (plastics or metals), or biodegradable materials like wood. Any new materials should be ethically sourced. 
2. Low energy. The robot should be designed to use as little energy as possible. It should have energy saving modes. If an outdoor robot then it should use solar cells and/or hydrogen cells when they become small enough for mobile robots. Battery powered robots should always be rechargeable. 
3. Repairable. The robot would be designed for ease of repair, using modular, replaceable parts as much as possible - especially the battery. Additionally the manufacturers should provide a repair manual so that local workshops could fix most faults. 
4. Recyclable. Robots will eventually come to the end of their useful life, and if they cannot be repaired or recycled we risk them being dumped in landfill. To reduce this risk the robot should be designed to make it easy to re-use parts, such as electronics and motors, and re-cycle batteries, metals and plastics.
   

These are, for me, the four fundamental requirements, but there are others. The BSI proposal adds the environmental effects of deployment (it is unlikely we would consider a sustainable robot designed to spray pesticides as truly sustainable), or of failure in the field. Also the environmental effect of maintenance; cleaning materials, for instance. The proposal also looks toward sustainable, upcyclable robots as part of a circular economy.

This is Ecobot III, developed some years ago by colleagues in the Bristol Robotics Lab's Bio-energy group. The robot runs on electricity extracted from biomass by 48 microbial fuel cells (the two concentric brick coloured rings). The robot is 90% 3D printed, and the plastic is recyclable.

 

 

 

 

 

I would love to see, in the near term, not only a new standard on Sustainable Robotics as a guide (and spur) for manufacturers, but the emergence of Sustainable Robotics as a thriving new sub-discipline in robotics.

Back to Robot Coding part 3: testing the EBB

In part 2 a few weeks ago I outlined a Python implementation of the ethical black box. I described the key data structure - a dictionary which serves as both specification for the type of robot, and the data structure used to deliver live data to the EBB. I also mentioned the other key robot specific code: 

# Get data from the robot and store it in data structure spec

def getRobotData(spec):

Having reached this point I needed a robot - and a way of communicating with it - so that I could both write getRobotData(spec) and test the EBB. But how to do this? I'm working from home during lockdown, and my e-puck robots are all in the lab. Then I remembered that the excellent robot simulator V-REP (now called CoppeliaSim) has a pretty good e-puck model and some nice demo scenes. V-REP also offers multiple ways of communicating between simulated robots and external programs (see here). One of them - TCP/IP sockets - appeals to me as I've written sockets code many times, for both real-world and research applications.  Then a stroke of luck: I found that a team at Ensta-Bretagne had written a simple demo which shows how to connect a Python program to a robot in V-REP, using sockets. So, first I got that demo running and figured out how it works, then used the same approach for a simulated e-puck and the EBB. Here is a video capture of the working demo.

So, what's going on in the demo? The visible simulation views in the V-REP window show an e-puck robot following a black line which is blocked by both a potted plant and an obstacle constructed from 3 cylinders. The robot has two behaviours: line following and wall following. The EBB requests data from the e-puck robot once per second, and you can see those data in the Python shell window. Reading from left to right you will see first the EBB date and time stamp, then robot time botT, then the 3 line following sensors lfSe, followed by the 8 infra red proximity sensors irSe. The final two fields show the joint (i.e. wheel) angles jntA, in degrees, then the motor commands jntD. By watching these values as the robot follows its line and negotiates the two obstacles you can see how the line and infra red sensor values change, resulting in updated motor commands.

Here is the code - which is custom written both for this robot and the means of communicating with it - for requesting data from the robot.

# Get data from the robot and store it in spec[]

# while returning one of the following result codes

ROBOT_DATA_OK = 0

CANNOT_CONNECT = 1

SOCKET_ERROR = 2

BAD_DATA = 3

def getRobotData(spec):

    # This function connects, via TCP/IP to an ePuck robot in V-REP

    # create a TCP/IP socket and connect it to the simulated robot

    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

    try:

        sock.connect(server_address_port)

    except:

        return CANNOT_CONNECT

    sock.settimeout(0.1) # set connection timeout

    

    # pack a dummy packet that will provoke data in response

    #   this is, in effect, a 'ping' to ask for a data record

    strSend = struct.pack('fff',1.0,1.0,1.0)

    sock.sendall(strSend) # and send it to V-REP

    # wait for data back from V-REP

    #   expect a packet with 1 time, 2 joints, 2 motors,   

    #   3 line sensors and 8 irSensors. All floats because V-REP

    #   total packet size = 16 x 4 = 64 bytes

    data = b''

    nch_rx = 64 # expect this many bytes from  V-REP 

    try:

        while len(data) < nch_rx:

            data += sock.recv(nch_rx)

    except:

        sock.close()

        return SOCKET_ERROR

    # unpack the received data

    if len(data) == nch_rx:

        # V-REP packs and unpacks in floats only so...

        vrx = struct.unpack('ffffffffffffffff',data)

        # now move data from vrx[] into spec[], while rounding floats

        spec["botTime"] = [ round(vrx[0],2) ] 

        spec["jntDemands"] = [ round(vrx[1],2), round(vrx[2],2) ]

        spec["jntAngles"] = [ round(vrx[3]*180.0/math.pi,2)

                              round(vrx[4]*180.0/math.pi,2) ]

        spec["lfSensors"] = [ round(vrx[5],2), 

                              round(vrx[6],2), round(vrx[7],2) ]

        for i in range(8):

            spec["irSensors"][i] = round(vrx[8+i],3)       

        result = ROBOT_DATA_OK

    else:       

        result = BAD_DATA

    sock.close()

    return result

The structure of this function is very simple: first create a socket then open it, then make a dummy packet and send it to V-REP to request EBB data from the robot. Then, when a data packet arrives, unpack it into spec, then close the socket before returning. The most complex part of the code is data wrangling.

Would a real EBB collect data in this way? Well if the EBB is embedded in the robot then probably not. Communication between the robot controller and the EBB might be via ROS messages, or even more directly, by - for instance - allowing the EBB code to access a shared memory space which contains the robot's sensor inputs, command outputs and decisions. But an external EBB, either running on a local server or in the cloud, would most likely use TCP/IP to communicate with the robot, so getRobotData() would look very much like the example here.