In a world where humans perform tasks that involve manipulating objects, such as lifting, dragging or interacting with them (for example, when we use our beloved mobile phones or we eat an apple), these actions are performed subconsciously, naturally. It is our senses that allow us to adapt our physical characteristics to the tasks instinctively. In contrast, robots act like little human apprentices, imitating our behaviour, as they currently lack the same awareness and intelligence.
To address this gap, Human Robot Interaction (HRI) emerged, a discipline that seeks to understand, design and evaluate the interaction between robots and humans. This field had its beginnings in the 1990s with a multidisciplinary approach but today its study is in constantly evolving and has given rise to important events1 that bring together visionaries in the field, who seek to promote this technology, bringing us ever closer to a world where artifical intelligence and humans understand each other and collaborate,transforming our near future.
Understanding the discipline of human-robot interaction is crucial. It is not a simple task; rather, it is tremendously challenging, requiring contributions from cognitive science, linguistics, psychology, engineering, mathematics, computer science, and human factors design. As a result, multiple attributes are involved:
Level of autonomy: decision making indepently
Exchange of information: fluency and understanding between different parts.
Different technologies and equipment: major adaptation between languages and models.
Tasks configuration: definition and execution of tasks efficiently.
Cognitive learning: abilities to learn and improve with time.
Here again, the type of interaction, is of particular importance, which is defined as a reciprocal action, relationship or influence between two or more persons, objects, agents, etc. and a key factor is the distance between human and robot, where it can be called a distance interaction, e.g. mobile robots that are sent into space, or a physical interaction, where the human being has contact with the robot.
These attributes are just a sample of the complexities involved in these robotic interaction systems, where interdisciplinary collaboration is essential for their evolution.
The challenges of interaction between humans and robots
At the moment the challenges are related to the highly unstructured nature of the scenarios where collaborative robots are used, as it is impossible for a technology developer to structure the entire system environment. Among the most important challenges aspects related to mobility, communications, map constructions and situational awareness.
So, what is the next step in human-robot interaction? Challenges include getting them to speak the same language and improving and simplifying communication, especially for non-technologically trained people, not presupposing these prior skills and not needing complicated instruction manuals; also discovering new forms of interaction, through natural language, in the case of assistive robots, special care for proximity and vulnerability; in general improving interfaces, making them more agile and flexible, so that they can be easily adapted to different scenarios and changes in the environment.
On the other hand, a challenge that has become particularly important in recent times, is to take into account emotional needs, human values and ethics in human-robot interactions, as highlighted in this HRI definition above:
HRI definition (Human-Robot interaction)
is the science that studies people’s behaviour and attitudes towards robots in relation to their physical, technological and interactive characteristics, with the aim of developing robots that facilitate the emergence of efficient human-robot interactions (in accordance with the original requirements of their intended area of use), but are also acceptable to people and satisfy the social and emotional needs of their individual users, while respecting human values (Dautenhahn, 2013).
Inspired by this exciting field of work, CARTIF, in collaboration with FIWARE Foundationand other leading partners in Europe, will start in 2024 the EuropeanARISEproject, which aims to achieve real-time, agile, human-centric, open source technologies that drive solutions in Human-Robot HRI interaction by combining open technologies such as ROS 2, Vulcanexus and FIWARE. And where the aims is to solve challenges by funding experiments that develop agile HRI solutions with increasingly adaptive and intuitive interfaces.
ARISE will address many of the following challenges: (1) Application of collaborative robotics for disassembly of value-added products, (2) Picking of complex products in industrial warehouses, (3) Flexible robotic collaboration for more efficient assembly and quality control, (4) Intelligent reprogramming ensuring adaptability for different products through intuitive interfaces, (5) Search and transport tasks in healthcare environments, (6) Improving multimodal interaction around different functional tasks, (7) Robotic assistance in flexible high-precision tasks, and (8) Improving ergonomics and worker efficiency, thus generating a multidisciplinary framework that takes into account both technological and social aspects.
In addition, the ARISE project opens its doors to robotics experts so that they can collaborate in solving the various challenges, thus generating new technological components for the HRI Toolbox, such as ROS4HRI. This collaborative grand challenge aims to make it easier for companies to create agile and sustainable HRI aplications in the near future.
1ACM/IEEE International Conference on Human-Robot Interaction, IEEE International Conference on Robotics and Automation (ICRA) y Robotics Systems and sciences
Have you ever tried a car racing game? An F1 race, a rally, or if you`ve tried driving Assestto Corsa, maybe you know where I am going with this little reflection.
If you have ever done so, you will have experienced a sense of “realism” of behaviour . In fact, if you have tried any driving simulator, you will have noticed the degree of detail and realism inthe behaviour of the simulation, being able to recreate to perfection, from different engine power and power delivery, weight distribution and vehicle dynamics. It is even able to recreate the type of surface on which the car is driving, which implies differences in behaviour, as is logical due to irregularities and different friction factors, etc. We could speak of digital twins, digital representations that are faithful to reality and that behave imitating the real case in the physical world.
Such is the degree of fidelity to reality, that the teams that spend the most moeny in the world to train their drivers, the F1 teams, train on virtual simulators (actually mixed, as the simulator is capable of transmitting dynamics to the driver).
The same could be said of airline pilots, who train for hundreds of hours on simulators that represent, with a very high degree of detail, the dynamics associated with flying an aircraft.
In industry, too, these virtual environments representing factories and their internal processes, known as digital twins, are being realised at an increasingly precise level of detail. And more and more companies, both on the customer side and on the side of the automation supplier, are implementing both the automation of a plant or process and simultaneously the digital twin. This is due to the benefits that can be obtained by having these tools available. For example, better decision making thanks to the possibility of prior simulation, flexibility and speed when implementing changes, more information in real time, improvements in maintenance.
If we train people on simulators and we emule processes and factories, can´not we do the smae with robots? Indeed, i think so.
If you have ever been involved in engineering in general, or in manufacturing processes, you will know that nowadays, the design of a product (service, building, road…) is done using specific design software, be it Autodesk, Blender or whatever, but it is done digitally.
Think of something you know perfectly well, a car. Because each and every one of its thousands of parts, whether they are in-house or supplied by suppliers, are correctly dimensioned (geothermally) and defined (properties, composition, materials…) digitally, both in 2D and 3D. If you integrate all the individual information in the concept, ‘car’, you would have there, the famous digital twin.
Now, extrapolating to a robot manufacturer (in this article, we are referring to service robots, not industrial robots), obviously although it is not as big an industry (as of today) and with as much baggage as the automotive industry, the design and manufacturing processes in the industry in general are very similar (in more incipient and modern industries, new trends are also integrated more quickly, primarily because of the size and culture), we can intuit that these companies may have or have a digital twin of their final product. With all the positive aspects that this entails for the company.
Well, at this point, you may ask, what does this have to do with Carlos Sainz training in a simulator? The answer is obvious, just as we train people to improve their skills using virtual environments, we are going to be able to train robot robots in such environments, with the great advantages that this entails. You will quickly see what I mean.
To train these robots, one of the techniques used is through the use of AI, putting the robot in a physical environment and trying to execute the tasks necessary to achieve the objective for which it has been programmed, and through deep learning, this robot learns to perform its mission better and better. For example: UNITED KINGDOM : Unveiling a robot that “learns on its own”.
Now, don’t just think of a simple robotic arm that performs simple tasks, and imagine more ‘futuristic’ robots, as in the illustration below (this is a commercial robot as of today).
If we have the digital twin (the most realistic and fully defined) of the robot, and we can recreate virtual environments that faithfully recreate physical environments, such as a city, a forest or the moon if you like. We will be able to train our robot in tasks and environments that could not be done otherwise (or would be more expensive, dangerous or outright impossible).
A couple of examples, a bit extreme, to make it easier to understand: We can recreate an area hit by a natural disaster and train these robots in rescue tasks. Or we can recreate Mars with its atmosphere, temperatures, gravity, terrain, etc., and see how the robot would behave in that environment.
Once the model is fully trained and satisfies the needs, the control model of the robot can be downloaded to the physical model. It can be trained as we have seen for events that have not yet happened. In this way, construction, material or design faults can be detected and fixed in the digital model, to check the effectiveness of the solution and subsequently improve the production process.
From the manufacturer’s side, the advantages of the digital twin and these training environments are clear. Flexibility, cost, time and risk savings, greater training capacity, greater customisation of the solution for the end customer, etc.
And for the end user, it would be very good, being able to train robots on specific tasks before they have to perform them, possibility of retraining on new policies, higher degree of personalisation, better training between unexpected agents.
I believe that this way of working could become a standard in the future. It is possible that tomorrow we will be training space miners to collect minerals on asteroids. Or we may be training robots to grow algae at depth.
Who knows what exciting missions we will send pre-trained robots on in the not-so-distant future.
Decarbonization is the “trending topic” of terms related to sustainability, energy and the environment. It is the process of reducing the amount of carbon dioxide (CO2) released into the atmosphere. Decarbonization means reducing climate change and dependence on fossil fuels, which are precisely those that emit CO2 when burned (clear examples are fuel-oil and coal). Decarbonization implies the use of cleaner energy sources, but also the adoption of technologies and methods to protect the environment and to reduce these emissions (the so-called “carbon footprint”).
However, what does this have to do with Cultural Heritage? Well, you will be surprised for sure, but it turns out that Heritage contribuyes many important things to decarbonization: the preservation of historical buildings, the reuse of spaces, the promotion of sustainable mobility, the promotion of cultural tourism and technological innovation in the assessment and the conservation of historical assets. In other words, it turns out that offers an environmentally friendly approach to urban planning and rural development.
If we go into a little more detail, you will see that Cultural Heritage can play a significant role in decarbonization and the fight against climate change. Here we provide you five ways to do so, but I´m quite sure your are able to think of some more (please tell us):
Technological innovation applied to conservation1 of historic buildings (where CARTIF has a lot to say): here the sensitivity required by historic buildings implies the development of specific techniques and technologies, which have broader applications in reducing carbon emissions in other fields of construction and environmental management. The digitally based technical inspection, the preventive conservation and the intervention involving H-BIM avoid both ruin and/or demolition, as well as new alternative constructions, which significantly reduces the material and energy resources to be used for these purposes. Furthermore, and this is worthy of remark, the old buildings were designed and built up with techniques and materials that are inherently sustainable, taking advantage of aspects that we are “rediscovering” right now such as orientation, natural ventilation and the use of native materials.
Reuse of spaces: Historical sites and buildings can be suitable adapted for new uses and transformed into living or working spaces with a level of comfort appropriate to the 21st century, which in the medium-long term saves resources compared to the construction of new substitute structures. This reuse contributes to greater energy efficiency and the reduction of carbon emissions.
Adaptation and transcription of ancient professional techniques: historic places are examples of how antique societies adapted to environmental challenges (which have always existed) and how lessons learned in the past can be adopted today through proper understanding and technological shift of traditional techniques and uses (both materials and methods).
Promotion of sustainable mobility: The preservation of historic centres in cities increasingly promotes sustainable mobility. In fact, they were desgined to move on foot, on horseback or in wagons and carriages. Therefore, they absolutely favour pedestrian accesibility and the use of public transport instead of private vehicles. This reduces dependence on fossil fuels and decreases greenhouse gas emissions.
Development of sustainable cultural tourism: it is more than proven that sustainable cultural tourism can play an important role in the local economy and even in the region, encouraging more environmentally friendly practices such as waste management, conservation of biodiversity and the promotion of quality agri-food and crafts.
But, does Cultural Heritage really do that much? Obviously yes. Indeed, a lot. In line with the priorities of the European Green Deal and the EU´s climate ambition for 2030 and 2050, the European Cultural Heritage Green Paper emerged in 2021, where indeed it is already considered a driver of decarbonization and mirror upon which citizens see themselves as key actors in the actions needed on this regard.
Historic building and decarbonization is a bionmial over which the Cultural Heritage & Regeneration Committee of the European Construction Technology Platform has been working for years (CARTIF takes part of the Executive Board). Its latest strategic research agenda for the period 2021-2027, promptly refers to this. And it is an issue that has been deepen into recent plenary assemblies. It is no wonder when 24% of the residential buildings in Europe date back to before 1945, nearly half of them have historical value, and of this latter, 73% are located in cities, which is precisely where the alrgest carbon footprint is made.
From now on, will you see Heritage with an additional view further than cultural, religious and tourist ones? Another thing for you to know.
1 In line with UNESCO and ICOMOS usage related to tangible heritage, conservation is considered as the umbrella term to cover a range of preservation, conservation, restoration, (re)use, interpretation and management activities.
In a world increasingly dependent on technology, the European Union has been embracing a focus on driving innovation, ensuring cyber security and strengthening its digital sovereignty. At the heart of this strategy is a commitment to open source software which is reflected in theOpen source software Strategy 2020-2023.
In parallel, the European Union has also carried out numerous antitrust and trade practice investigations related to large technology companies. Thus, in 2018, the European Commission imposed a record fine of €4.34 billion on Google for abuse of dominance with android, in 2020 opened an investigation into Apple´s App Store practices, in 2021 Facebook was investigated for its use of the data it collected to gain an advantage over its competitors. And, in 2023 Microsoft has been accused by the EU of imposing Teams on Office users.
In this article, we will briefly explore the main points of the European Comission´s strategy to harness the power of open source software and identify some of the results and achievements that support this initiative.
The European Commission with open source software
The European Commission has recognised the value of open source software as a key handle for achieving its technological and digital goals. It has therefor focused the strategy on the following points:
Promotion of open source software in Public Administration: the Commission launched the open source repository for the EU institutions: code.europa.eu in order to, according to its IT General Director, Veronica Gaffey, “move from being an organisation that consumes software to one that builds its own solutions…”
Investment on open source projects: the European Commission has asigned funds through the H2020 programme to support and encourage open source research and developmen projects.
Improvement of the cibernetic security: the strategy includes security audits of open source project used in the EU´s technology infrastructure through the FOSSA (Free and Open Source Software Auditing) initiative. These audits have helped to identify and correct security vulnerabilities, thus strengthening cyber security in Europe.
Promoting collaboration and community developer: one of the initiatives in this regard has been the European Commission´s collaboration with GitHub to provide students and teachers with free access to GitHub Education, which has fostered training in open source software development and thus European talent.
Digital sovereignty: to reduce dependence on foreign technologies, strengthening the EU´s digital sovereignty.
Interoperability and open standards: by promoting open standards and interoperability to ensure that EU systems are compatible and share data efficiently. An example of this has been the Joinup platform which fosters the exchange of open source solutions and offers reusable software components.
In short, the European Commission, through its open source software strategy aims to promote open source to boost innovation, cyber security and interoperability in the European Union, as well as to strengthen Europe´s digital sovereignty
Results and impact
Although, it is not easy to obtain concrete figures on the impact that theEuropean Commision´s open source software strategy is having, it is possible to list in general terms some of the achievements:
Significative economic savings: the adoption of open source software in public administration has led to considerable savings in software licensing costs estimated at several million euros per year.
Strengthening of the cyber security: FOSSA security audits have identified and addresses critical vulnerabilities in open source software projects used in the EU, improving cyber security in the region.
Better interoperability: the adoption of open source software has improved interoperability between systems across the EU public administration, facilitating collaboration and data exchange between member countries.
Fostering the innovation: investment in open source software projects through the Horizon 2020 programme has stimulated innovation in key areas, such as artificial intelligence, cyber security and cloud computing.
Resistance from organisations and users
The strategies deployed for years by large technology companies – allowing the use of services for free without restrictions and based on increasingly closed ecosystems and even on the acquisition of emerging services with the possibility of competing or threatening their supremacy – continually creates users and companies dependent on their products that, due to resistance to change, try to avoid the use of other services that are more unknown to them, and that prevent other technology players with limited resources, but with great ideas, from competing on equal terms and offering interesting products.
At CARTIF, as an affiliated institution of RedIris, Spanish academic and research network that provides advanced communications services to the national scientific and university community and that also promotes the development of free software knowledge in the academic-scientific environment, we are convinced of the benefits of using open source software and therefore we try to use and support the technological tools and services that this institution offers. In addition, we also develop our own tools as a strategy to motivate, attract and maintain talent through the generation of knowledge, and we raise awareness and promote the use of open source software tools among our users over the services and platforms of large technology companies, something that is not always easy due to the resistance to change of organisations and users.
In recent decades, the evolution towards a genuine energy and environmental transition has taken a fascinating course. Our social and productive system is undergoing an unprecedented transformation, and the major issues that characterise the 21st century, such as energy, digital security and socio-economic issues, among others, cannot be addressed separately. This is precisely why the digital transformation today offers new ideas and opportunities also in the purely energy field. The power of data is now obvious to scientists, engineers and economists, but it can be beyond the reach of ordinary citizens, who often lack the means to understand how much this tool can help them in their daily lives. A concrete example is how, using data collected by smart meters installed in our own homes, we can actively monitor and modify consumption profiles, whether for electricity, water or gas, to the benefit of the environment and of our wallet.
Previously, the energy market was centralised and mainly driven by a few large suppliers. However, it is now undergoing a decentralisation and orientation more in line with the real needs of individual consumers. The individual, once a mere passive recipient of energy services at home, can now aspire to be actively involved in the various stages of the production process thanks to the integration of renewable technologies into local grids and the development of Renewable Energy Communities (REC). This change in the traditional perspective of our energy market is already underway.
In this context, the energy prosumer is the key figure in each REC, combining the more traditional producer and consumer. The prosumer can cover their energy demand as independently as possible from the grid, taking advantage of their self-consumption and storing or selling the surplus to the grid. In a renewable energy community, this surplus production can be used to meet the energy demands of other members. All this implies the need for the prosumer to be aware of the production process in which he/she is involved and the functioning of the energy market.
Being active citizens and possibly involved in renewable energy communities has significant implications from a social perspective. Strata of the population with limited accees to energy supplies, either due to financial means or difficult access to the grid for geographical reasons, could benefit most substantially from local production and the formation of energy communities. The active participation of individual citizens in decision-making processes generates notable benefits, among which are, without a doubt, greater acceptance of renewable energies, as well as a greater social cohesion in the community, which by its very nature is democratic and equitable, overcoming disparities associated with gender, age or individual economic capacity. On the other hand, from an economic point of view, it is crucial to highlight that self-consumption of energy leads to significant savings in energy bills, due to a lower purchase of electricity from the grid. Furthermore, the formation of energy communities can mobilise capital at the local level and attract investment.
Within the framework of Horizon Europe, the European Union(EU) research and innovation (R&I) programme for the period 2021-2027, CARTIF is involved in the ENPOWERproject. We want to contribute to the energy activation process of European citizens and to the development of renewable prosumer communities through innovative data-driven strategies. On the one hand, it is crucial to identify the impacts of the project considering parameters covering both social and environmental factors. On the other hand, we seek to assess the level of commitment of engaged European citizens, with the explicit aim of fostering the cohesion of individuals towards energy independence.
What is generally understood by Artificial Intelligence – or AI?. It is a pervasive term nowadays, that appears not only in secluded and obscure academic circles hidden from the rest of society. Most of us have, to some degree, already heard the term. AI is not just-yet-another buzzword; and it is here to stay. This is not really news: many examples of success stories of AI-based systems have been hugely popularized by media (AlphaGo, DeepBlue, Chat-GPT, to name a few). The boom of Deep Learning and its application to an extremely wide spectrum of areas have also helped masively spread the word in very recent years. Because AI is transversal; AI technology is most of the time task-agnostic. This means that AI methods are susceptible to be used to support an enormous range of very different applications and problems.
And, while this is absolutely true, there is an important gap between what AI means to people working in AI research and to people outside of it (general public, policy makers, technological companies, different economic sectors…). Don´t misunderstand me, such a gap is natural: specialists in any area have their own jargon to address subjects in a specific manner compared to non-specialists. But I also think that 1) efforts to close this gap and demystify AI can have a very positive impact, and 2) this can foster a much better ecosystem for research and innovation in all economic and societal sectors, and at local, national and international levels.
Here are my two cents on this: a large part of the gap comes from linguistics. Take this opinion with a grain of salt and draw your conclusions from the AI-based systems that you might come across.
I bet you have probably heard, whether in the news, when taking to friends, or when working on a project involving AI, a statement similar to:
"This system is an AI. This AI is intelligent. The AI does this like a human specialist would"
I have three problems with these statements, and the problems are linguistic, not technological. I will explain them from a practical point of view using an example of a system that includes AI methods in a project from the area of Health and Wellbeing at CARTIF: a robot of the Temi model (called Matías) in EIAROB project, which is a social robot that will be deployed in the homes of elderly people during the project, to help monitor and accompany them, thus enhancing the people´s wellbeing, reducing solitude, and extracting information about the people´s health status and its progression for their medical professionals. In this context, the capabilities of the Temi robot are described in the following figure.
The three issues that I have with the previous statements about AI are:
An app or an artificial system is not and AI or an Artificial Intelligence; the app or system might include elements that use AI methods or may be based on AI; AI is a field of study, as is for example Physics, Chemistry or Biology. For example, the Temi robot is not an Artificial Intelligence; it includes elements that use AI methods (in green in the figure, such as speech recognition and generation for conversation).
Saying that an artificial system is intelligent, while absolutely valid, is a language shortcut that we must be aware of. The system might use AI mechanisms, but they don´t function in the same manner as a person said to be intelligent. An effort should be generally made in trying to avoid putting human properties on artificial systems gratuitously (anthropomorphizing). For example, saying that the Temi robot is intelligent because it can maintain conversations is a language shortcut; it includes AI (Natural Language Processing) methods that are able to interact in plain language with humans, but these methods are actually statistical, and are not an actual representation on how humans process language in their brains.
An AI-based system delivers a function. It is possible that the system includes mechanisms, methods or algorithms that are inspired, mimic or model how humans would approach the problem. But ultimately, the system does not use the same process that humans use. It contains elements that might have been designed thinking of how humans would approach a problem (for example, based on math, statistics, logic or computations), but they make abstractions and approximations and, as such, cannot be said to do things the way humans do. For example, some of the mechanisms used by Temi robot to talk with humans are based on artificial neural networks, which is a set of AI mathematical and statistical methods that allow to recognize and generate language in the conversation. Such artificial neural networks methods, in their inception (1950s-1970s), were very loosely inspired by how neurons work in the biological brain, but do not aim to reproduce it.
These factors will not change the way AI-based systems are created , but they could change the way we think of AI-based systems. Thus, rather that speaking of “Artificial Intelligences” as systems, I advocate for talking of “systems with AI methods” or “AI-based systems”, and consciously trying to be clear and specific when speaking of AI. Let´s not forget that the purpose of any artificial system is to have a positive impact in people´s lives. People should be at the center of all engineering efforts; consequently, AI-based systems should be thought of as tools to support people and be understood as such.
In CARTIF, we work on a wide spectrum of projects that involve applied AI for different purposes, and at every step of our R&D&I efforts we keep our eyes on the goal of creating or improving processes that ultimately will serve people and society. As such, the systems that we create and develop in these projects are systems with AI methods, or AI-based systems, but they are not “Artificial Intelligences”.