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Robotics call for proposals

Pursuing the future of robotics research.

About this CFP

Amazon is advancing the robotics technology in many application areas. Amazon Robotics designs, codes, builds, and manufactures game-changing software applications, control systems, robotics and related hardware that is revolutionizing Amazon's operations across the globe. AWS RoboMaker provides the most complete cloud solution for robotic developers to simulate, test and securely deploy robotic applications at scale. We welcome proposals in these research topics related to robotics:

• Human-Robot Interaction (HRI) - including human machine interaction and collaboration, learning from human preferences, affective and social interactions, and ergonomic or cognitive load support;
• Autonomous Navigation and Mobility - including field robotics, SLAM, long-term autonomy, trajectory planning, autonomous calibration, methods for ground and aerial applications, sim-to-real transfer, real-to-sim digital twin creation, sensor simulators, wireless communication, localization systems, proximity sensors, and low power devices;
• Manipulation - including grasping, dexterous manipulation, gripper design, motion and grasp planning, tactile sensing, compliant control, manipulation learning, assembly, multi-step task planning, sim-to-real transfer, real-to-sim digital twin creation, simulation of deformable objects, and sensor simulators;
• Multi-robot systems - including multi-agent pathfinding, task assignment, planning and scheduling, distributed algorithms, and multi-agent reinforcement learning;
• Artificial Intelligence for Robotics - including computer vision, semantic scene understanding, pose estimation, object tracking, multi-modal sensing, calibration-less operation, few-shot learning, reinforcement learning for robotics, sample-efficiency, deep learning, and hierarchical RL.

Theoretical advances, creative new ideas, and practical applications are all welcome.

The submission period has closed. Decision letters will be sent out early 2021.

Award details

Selected Principal Investigators (PIs) may receive the following:

• Unrestricted funds, no more than $80,000 USD on average
• AWS Promotional Credits, no more than $20,000 USD on average
• Training resources, including AWS tutorials and hands-on sessions with Amazon scientists and engineers

Awards are structured as one-year unrestricted gifts. The budget should include a list of expected costs specified in USD, and should not include administrative overhead costs. The final award amount will be determined by the awards panel.

Eligibility requirements

Please refer to the ARA Program rules on the FAQ page .

Proposal requirements

Proposals should be prepared according to the proposal template . To submit a proposal for this CFP, please also indicate your research topic(s) as outlined in the “About this CFP” section, and list the open-source tools you plan to contribute to, and/or any AWS tools you may use.

Please note when submitting a proposal, you will be asked to select a subcategory from these five options: Manipulation, Mobility, Human Robot Interaction, Artificial Intelligence for Robotics, and Multi-robot Systems.

Selection criteria

ARA will make the funding decisions based on the potential impact to the research community and quality of the scientific content.

Expectations from recipients

Recipients are assigned an Amazon research contact who offers consultation and advice along with opportunities to participate in Amazon events and training sessions. To the extent deemed reasonable, Award recipients should acknowledge the support from ARA. Award recipients will inform ARA of publications, presentations, code and data releases, blogs/social media posts, and other speaking engagements referencing the results of the supported research or the Award. Award recipients are expected to provide updates and feedback to ARA via surveys or reports on the status of their research. Award recipients are encouraged to attend (physically or virtually) our Robotics Research Symposium in fall 2021 to present the results of their research. Award recipients will have an opportunity to work with ARA on an informational statement about the awarded project that may be used to generate visibility for their institutions and ARA.

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This CFP is funded annually.

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Research Proposals

In September 2022, the TCH awarded research grants to young researchers to carry out research in the field of haptics.

These grants were awarded to young researchers at the postdoc and Ph.D. student position levels to foster curiosity from applicants to explore new ideas in their research project that otherwise will be left unexplored. Below are the award recipients and their project titles: 

• Abigail Nolin (University of Delaware, USA), Fingertip contact mechanics on smooth, chemically-modified interfaces to control fine touch
• Anika Kao (University of Virgina, USA), Comparing states of skin deformation, and perceptual equivalence, between con-contact airflow and mechanical contact stimuli
• Muge Cavdan (Giessen University, Germany), Do you perceive what you feel?: Affective influences on perceived roughness
• Olivia Leslie (University College Dublin, Ireland), A tactile sensing array with side-sensing for collision detection and grasp exploration
• Ori Fartook (Ben Gurion University of the Negev, Israel), Drones as emotional support technology: using Haptics to create first contacts
• Robert Kirchener (TU Dresden, Germany), Rendering strategies for plausible affective haptics
• Sandeep Kollannur (University of Southern California, USA), Toolkits for haptic harness
• Slyvia Tan (Northwestern University, USA), Development of soft, wearable haptic pucks
• Thomas Daunizeau (Sorbonne University, France), On the evolutionary role of emotional sweating in gripping

We received 28 proposals, and through a rigorous peer review process were able to award nine top proposals, granting $2,000 USD to each. 

We thank the evaluation committee:

• Yasemin Vardar, TCH co-chair (TU Delft, The Netherlands)
• Claudio Pacchierotti, TCH emeritus co-chair (CNRS, France)
• Rebecca Fenton Friesen (Texas A&M University, USA)
• Cara M. Nunez (Harvard University, USA)
• Dangxiao Wang (Beihang University, China)
• Yitian Shao (TU Dresden, Germany)
• Guhnyuk Park (Gwangju Institute of Science and Technology, South Korea)

 We are looking forward to seeing the fruits of these efforts soon!

Foundational Research in Robotics (Robotics)

The Foundational Research in Robotics (Robotics) program supports research on robotic systems that exhibit significant levels of both computational capability and physical complexity. For the purposes of this program, a robot is defined as intelligence embodied in an engineered construct, with the ability to process information, sense, and move within or substantially alter its working environment. Here intelligence includes a broad class of methods that enable a robot to solve problems or make contextually appropriate decisions. Research is welcomed that considers inextricably interwoven questions of intelligence, computation, and embodiment. Projects may also focus on a distinct aspect of intelligence, computation, or embodiment, as long as the proposed research is clearly justified in the context of a class of robots. 

The focus of the Robotics program is on foundational advances in robotics. Robotics is a deeply interdisciplinary field, and proposals are encouraged that explore the full range of fundamental engineering and computer science research challenges arising in robotics. However, all proposals must convincingly explain how a successful outcome will enable transformative new robot functionality or substantially enhance existing robot functionality. The proposal should clearly articulate how the intellectual contribution of the proposed work addresses fundamental gaps in robotics. Meaningful experimental validation on a physical platform is strongly encouraged. Projects that do not represent a direct fundamental contribution to robotics should not be submitted to the Robotics program.

Potential investigators are strongly encouraged to discuss their projects with a Robotics Program Officer before submission. Non-compliant proposals may be returned without review.

Full Proposals Accepted Anytime After August 1, 2020. Declined proposals may be resubmitted to the Robotics program after a minimum moratorium period of one year from the time of initial submission, regardless of the Units of Consideration for the original and resubmitting proposals. As required by the PAPPG, declined proposals must be substantially revised prior to resubmission. Principal Investigators are strongly encouraged to consult with a cognizant Program Officer before resubmitting a previously declined proposal.

Robotics proposals submitted to other program announcements or solicitations, including the Faculty Early Career Development Program (CAREER), must meet the respective deadlines of those programs; please refer to the deadline dates specified in the appropriate announcement or solicitation. Proposals for EArly-concept Grants for Exploratory Research (EAGER), Rapid Response Research (RAPID) or Research Advanced by Interdisciplinary Science and Engineering (RAISE) can be submitted at any time, but Principal Investigators (PIs) must contact the cognizant Program Officer prior to submission. Proposals for supplements or workshops, or any other type of grant, can be submitted at any time, and PIs are encouraged to contact the cognizant Program Officer prior to submission.

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Caring in the in-between: a proposal to introduce responsible AI and robotics to healthcare

• Open access
• Published: 16 December 2021
• Volume 38 , pages 1685–1695, ( 2023 )

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• Núria Vallès-Peris   ORCID: orcid.org/0000-0003-4150-761X 1 &
• Miquel Domènech   ORCID: orcid.org/0000-0003-2854-3659 1  

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In the scenario of growing polarization of promises and dangers that surround artificial intelligence (AI), how to introduce responsible AI and robotics in healthcare? In this paper, we develop an ethical–political approach to introduce democratic mechanisms to technological development, what we call “Caring in the In-Between”. Focusing on the multiple possibilities for action that emerge in the realm of uncertainty, we propose an ethical and responsible framework focused on care actions in between fears and hopes. Using the theoretical perspective of Science and Technology Studies and empirical research, “Caring in the In-Between” is based on three movements: the first is a change of focus from the world of promises and dangers to the world of uncertainties; the second is a conceptual shift from assuming a relationship with robotics based on a Human–Robot Interaction to another focused on the network in which the robot is embedded (the “Robot Embedded in a Network”); and the last is an ethical shift from a general normative framework to a discussion on the context of use. Based on these suggestions, “Caring in the In-Between” implies institutional challenges, as well as new practices in healthcare systems. It is articulated around three simultaneous processes, each of them related to practical actions in the “in-between” dimensions considered: monitoring relations and caring processes, through public engagement and institutional changes; including concerns and priorities of stakeholders, with the organization of participatory processes and alternative forms of representation; and making fears and hopes commensurable, through the choice of progressive and reversible actions.

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1 Introduction

In the landscape of imaginaries that surround artificial intelligence (AI) and robotics in healthcare, there is a prominent debate regarding utopian and dystopian scenarios, whereby social and ethical discussions are polarized between the promises and the dangers of AI. This paper is grounded in a realm that is far removed from the so-called ‘abyss’ between utopias and dystopias, and is instead situated in the terrain that lies between the fears and hopes, and where the multiple possibilities are experienced (de Sousa Santos 2016 ). Using the theoretical approach of Science and Technology Studies (STS), we propose a set of actions in these in-between spaces to introduce responsible AI and robotics to healthcare. However, we do not propose any kind of action, but actions based on the ethics of care. We do so by exploring a particular case that could exemplify some of the common dynamics of medical AI, namely that of care robots in a children’s hospital (i.e., socially assistive robots).

1.1 The narrative of promises and dangers in AI

There is an increasing narrative in public and academic domains that assumes that the solution to the main clinical, economic, and general well-being problems that healthcare systems are facing around the world will come from AI (Morley et al. 2020 ; Topol 2019 ). Medical AI offers plenty of promise: early diagnosis using image analysis in radiology, pathology, and dermatology, with excellent diagnostic speed and accuracy when working in parallel with medical experts; personalized treatments that optimize the care trajectory of chronically ill patients; precision therapies for complex illnesses; reduction of medical errors; and greater enrolment for clinical trials (Miller and Brown 2018). Leading healthcare and computer scientists have argued that AI will help healthcare systems achieve greater efficiency in two ways: on the one hand by improving the timeframe and accuracy of patient diagnosis and treatment and, when possible, helping with early prevention; and, on the other hand, streamlining the workload, using staff more efficiently (Kerasidou 2020 ). Robotics is considered to be a specific sub-category of medical AI (Shoham et al 2018 ). In the time of so-called New Robotics, a new family of robots devoted to healthcare applications has become particularly prolific (Schaal 2007 ). This new era of robotics enables safe robot interactions with humans, easy programming and adjustment to particular needs, and the possibility for robots to function as mobile, interactive information systems in hospitals, nursing homes and other healthcare settings. Due to their capacity to interact with humans, these robots open up an interesting scenario for working on the emotional and social dimensions of health.

The impetus of AI and robotics science, and the deep learning subtype in particular, facilitated by the use of so-called big data, along with highly enhanced computing power and cloud storage, enables greater automation in a variety of sectors. However, although automation has a long-standing impact on employment, productivity and the economic performance of companies and nations, the introduction of AI systems to healthcare environments has had an unprecedented impact on the automation of care. (Vallès-Peris and Domènech 2020 ; Sampath and Khargonekar 2018 ). Thus, along with the narrative on the promises of AI, these innovative technologies are also the source of many of the uncertainties of our time.

The field is certainly high on promise and relatively low on data and proof. Moreover, many problems have been associated directly to AI and robotics, such as social bias, privacy and security, and lack of transparency (Topol 2019 ). The possibility of machines performing activities that are traditionally associated with the exclusively human condition -such as abstract problem-solving, perceptual recognition, social interaction and natural language (Vallor and Bekey 2017 )-generates many dangers. These dangers relate to a hypothetical scenario of future AI functionalities, rather than real, current ones, and are associated with the “supposed” possibility of humans being replaced by technological systems or of care for dependent and ill persons becoming "dehumanised" (Vallès-Peris et al. 2021a ).

Although it is undeniable that the development of AI and robotics is exponentially real, there is a gap between the ethical concerns around its supposed functionalities and the real possibilities of contemporary AI (Hagendorff 2020 ). If the debate focuses only on a utopian or dystopian development of robotics, some issues are exaggerated, while the identification of contexts, situations and controversies regarding artefacts that do not respond to this promised future remains unclear. At the same time, it is difficult to identify problems and generate an ethical debate around some robots that are already in use (that do not integrate the complex functionalities and capabilities expected of highly intelligent autonomous robots).

As a proposal to go beyond this scenario of promises/dangers, we develop an approach to introduce responsible AI and robotics to healthcare based on three movements: (a) the first is a change of focus from the world of promises and dangers to the world of uncertainties; (b) the second is a conceptual shift from assuming a relationship with robotics based on a Human–Robot Interaction (HRI) to another focused on the network in which the robot is embedded (the “Robot Embedded in a Network-REN”); and the last is (c) an ethical shift from a general normative framework to a discussion on the context of use, from the logic of the ethics of care. Based on these suggestions, we define a new ethical–political proposal for introducing responsible AI and robotics, what we call “Caring in the In-Between”.

In order to develop our proposal, this paper is organized in seven sections. The one that follows contextualizes our research on care robots; we then develop the three movements on which we base our proposal: in Sect.  3 we focus on the movement from the world of promises and dangers to the world of uncertainties, in Sect.  4 we explore the movement from HRI to a REN relation and, in Sect.  5 we explain the movement from an general ethical framework to the context of use, from the ethics of care; in Sect.  6 we outline our ethical–political proposal to introduce responsible robotics and AI to healthcare and; finally, we summarize the main conclusions.

2 Empirical research on care robots in a children’s hospital

Our proposal for responsible AI and robotics is based on a series of empirical studies that we carried out on the introduction of care robots to a children’s hospital in Barcelona. In children’s health settings, robots for care are typically considered to be those that are used in specific therapeutic interventions, e.g., with autistic children (Heerink et al 2016 ) or in rehabilitation processes (Meyer-Heim and van Hedel 2013 ). Care robots are also those involved in the extensive line of research and application of robots to reduce pain and anxiety when children are subjected to some type of intervention (Crossman et al 2018 ) or simply to make hospital stays more pleasant (Díaz-Boladeras et al 2016 ). In engineering and computer science, the care robots to which we refer in this paper are usually included in the field of so-called “socially assistive robotics”. In general terms, this field is focused on providing artificially intelligent robotic systems to aid end-users with special physical or cognitive needs in their daily activities (Pareto Boada et al. 2021 ).

Since 2015, we have been involved in the research being done by a children’s hospital in Barcelona to design and introduce care robots. As social scientists, our work together with an interdisciplinary team of physicians, nurses and engineers involved identifying the ethical and social implications of introducing such robots to the hospital. Throughout the process, we conducted three empirical research projects that shared the common objective of exploring the fears and hopes of the different actors that, in one way or another, established or would have to establish a relationship with a robot for care.

Some details of these research projects are the following:

A participatory process with children to design a robot for care for a children’s hospital. For three months, we conducted a participatory process in a school with 60 six-year-old children. Children were organized into two large groups of 30, and 12 sessions were held with each group (24 sessions in total). The participatory process employed design-thinking techniques.

A Vision-Assessment process to identify the risks and benefits associated with future visions of robots for care among the different categories of people involved in care at a children’s hospital. We organized: three focus groups with nurses, with a total of 22 people; three focus groups with hospital volunteers, also with 22 people taking part; one focus group with the relatives of hospitalized children—after previously holding a workshop with them on robotic technologies in care—with 10 people participating; and three interviews with physicians who were heads of units at a children’s hospital.

A set of interviews with roboticists to identify their concerns regarding robots for care. Eleven face-to-face semi-structured interviews were conducted with roboticists working in the field of care robots.

The framework of reference for the various participants and the fieldwork carried out during the investigations, was the robots that were being implemented in that hospital (already introduced or in pilot/prospective phases): pet robots used to reduce anxiety in pre-operative and diagnostic tests, as well as to make the hospital stay more pleasant for children with long admissions; therapy robots for children with autism and; tele-communication robotic systems, for diagnostic visits by doctors or to facilitate communication with their school in the case of hospitalised children. This does not mean that our proposal refers only to this type of robot, but that our proposal is nurtured by a study of the implementation of these artefacts. The fact that our study was carried out on robots used with hospitalised children meant we could identify particularly sensitive issues and highly delicate situations more easily.

We analysed the collected data using qualitative techniques, i.e. thematic analysis for the interviews and focus groups (Clarke and Braun 2014 ) and thick description for the participatory process (Ponterotto 2006 ). However, the goal of this paper is not to present the analysis and results of these three research projects, but to build a responsible approach to AI and robotics that embraces the practices and concerns of the different groups of actors identified in those projects.

3 From the world of promises and dangers to the world of uncertainties

As explained in the introduction, the social and ethical debates surrounding AI and robotics are often articulated around utopian or dystopian scenarios (Shatzer 2013), or focused on the identification of risks and benefits (Verbeek 2006 ), which contributes to a growing polarization of promises and dangers. As sociologist de Sousa Santos ( 2016 ) warns, in our technologically modified world, fear and hope are collapsing in the face of the growing polarization of hope without fear (the health innovation sector and markets with exponential benefits) and fear without hope (anyone who believes that technological progress cannot be stopped and that we will need to adapt to whatever changes may come about). In this situation, uncertainties become abysmal, and leave no room for action.

Inspired by Spinoza's notions, de Sousa Santos ( 2016 ) examines the unequal epistemological and experiential distribution of fear and hope. In addition to the idea of abysmal uncertainties, uncertainty is also the experience of the possibilities that arise from the multiple relationships that can exist between fear and hope. Since the relationships between different groups and actors are different, the types of uncertainty are also different. Fear and hope are not equally distributed among all social groups or historical epochs. In relation to technologies, fear and hope are defined by parameters that tend to benefit social groups that have greater access to scientific knowledge and technology. For these groups, the belief in scientific progress and innovation is strong enough to neutralise any fear regarding the limitations of current knowledge. For those who have less access to scientific and technological knowledge, this is experienced as inferiority that generates uncertainty. For them, uncertainty is generated by their place in a world that is defined and legislated by powerful and alien knowledge that affects them and over which they have little or no control. This knowledge is produced about them and eventually against them, but never with them (de Sousa Santos 2016 ).

In the dreamscape of promises and dangers that surround medical AI, we defend the need for ethical and social discussion to move away from a speculative scenario that considers an abyss between utopias and dystopias. In contrast, we situate ourselves in the terrain that lies in between fears and hopes, where multiple possibilities arise (de Sousa Santos 2016 ). In this terrain, uncertainty is situated and negotiated, embedded in the actors that participate in healthcare relations. So, in order to develop more responsible AI in healthcare, which is particularly addressed at social needs and guaranteeing individual and collective well-being, we propose a focus on care (caring as action) in the spaces in between that are opened by a particular artifact (i.e. robots for care in paediatric hospitals).

The literature on ethics and robots primarily addresses the interests of what the community focused on study in this field considers important, while the opinions of other stakeholders, such as robotics researchers, health professionals and patients, are not directly discussed. In the public debate, questions about whether humans are replaceable by robots or whether robots should have rights seem to take precedence over the current and more real challenges that could arise from their use in specific applications (van der Plat et al. 2010 ). This “a priori” philosophical approach has some limitations (Stahl and Coeckelbergh 2016 ), including the difficulty of establishing a normative framework to guide and orient everyday problems that may arise in hospitals or other health settings when care robots are introduced.

Our proposal to go beyond a discussion centred on the promises of AI is based on an analysis of how fears and hopes are articulated and negotiated with different actors. While looking into the controversies that the several actors involved in care processes with robots have to deal with, we came to study the practices and values of the different actors that participate in healthcare relations in a children’s hospital. Callon et al ( 2009 ) maintain that the controversies over the use of certain technologies create uncertainty and bring about unforeseen concerns. Instead of simply seeking consensus or general principles, if we wish to enrich the debate around technologies, it is important to call attention to the importance of collective discussion of the matter. As observed by Epstein ( 1995 ), the participation of non-specialists in the development of knowledge that actually concerns them may lead to problems being formulated and research results being disseminated and implemented in a different way. Following this view, what we try do here with regard to care robots is to reflect the importance of collective discussion of the controversies surrounding medical AI to thereby enrich the formulation of problems and propose alternative frameworks.

Given that the level of innovation in robotics and AI is accelerating and surpassing our capacity to anticipate its consequences and influences on our lives, the responsible introduction of these technologies to healthcare is situated in the realm of uncertainty that lies between fears and hopes. In these contexts of the uncertain impact of technologies, Callon et al ( 2009 ) call for precaution with regard to the potential harm, impact or causal relation of a technological innovation. The action that may better represent precaution is the so-called “measured action”, meaning a progressive action motivated by feedback and constant debate that considers the consequences of that action. “Measured action implies an active, open, contingent and revisable approach, exactly the opposite of a clear final decision. And then, this approach rests on a deepening of knowledge, but not only of the knowledge provided by the scientific disciplines of isolated research. The proportionality of actions for society, acceptability and economic cost also have their place in deliberation” (Callon et al. 2009 : 210).

This position leads us away from the realm of abysmal uncertainties between utopian promises and dystopian dangers. This is uncertainty about desired futures, about what a care robot is, about the risk of introducing such devices to children’s care, and so on. In our proposal, uncertainty and precaution are the most relevant characteristics of AI and robotics, and are thus a privileged space for responsible introduction of these technologies to healthcare. This idea is grounded on an STS theoretical background and on a particular way of understanding robots, as well as on the centrality of the ethics of care when considering responsible technologies. In the following sections, we develop each of these issues.

4 From human–robot interaction (HRI) to a robot embedded in a network (REN)

Care robots are used to perform a number of specific tasks-such as facilitating the relationship between patients and places outside the hospital, reducing distress in pre-operative care, and modifying the ways that healthcare staff perform certain actions—such as measuring vital signs, performing detailed interventions, etc. When thinking about and designing a robot’s agency in medical settings, as well as its ability to interact with children, it is relevant to take into account how artefacts or devices are embedded in a network of caring relationships that involves several actors(López-Gómez 2015 ). Children’s well-being is not only defined by the robot’s ability to interact with them, but also by its ability to interact with the whole social system of care relationships. However, in engineering and the related ethical debates, the relationship between children and AI robots is often conceptualized by the notion of Children-Robot Interaction (CHRI), which is the children’s equivalent of Human–Robot Interaction (HRI), and which describes two isolated entities interacting with each other: a human and a robot.

Most STS approaches, such as the Actor Network Theory and social constructivism of technology, postulate a more or less open ontological universe in which it is difficult to establish rigid boundaries between the social, the human, the natural and the technological (Karakayali 2015 ). With the abandonment of the notions of nature and society as separate entities, a new entity emerges: a heterogeneous network (Callon and Latour 1992 ). In this view, any technological innovation is explained by its relational and contextual nature, i.e. how it makes sense in its network of relationships (Domènech and Tirado 2009 ). Therefore, a technological innovation is not only an artefact, but a whole network of devices, processes and actors (Latour 1999 ). From this perspective, care robots are a conglomerate of material, social and semiotic relations in which technical, scientific, political, economic, social and ethical considerations are intimately entwined within a single actor (Latour 1999 ). This idea of heterogeneity can be accompanied by relational materialism, according to which the elements do not exist for reason of any essence but are constituted from the networks of which they are part. This approach could be carried to its logical conclusion by assuming that objects, entities or actors are nodes in a network. These nodes are also constituted interactively, and do not exist outside their interactions. Artefacts, people, institutions, protocols… everything is an effect or a product (Law and Mol 1995 ).

Some authors use the concept of interpretive flexibility to classify robots for care. According to this concept, a robot could be classified by its context of use, the function for which it is used, and the user (Howcroft et al 2004 ). This notion highlights the impossibility of separating the definition of technical problems from the socio-economic framework to which they are associated (Bijker 2009 ). Therefore, a robot can be called a care robot when it is used in a hospital to reduce children’s anxiety in preoperative spaces, but the same robot can also be classified as an entertainment robot when it is used by engineering students to compete in international robot soccer leagues. Likewise, a robot that is used by nursing staff to lift patients with little or no mobility can be classified as a robot for care, but when it is used by workers in a factory to lift heavy objects, it can be considered an industrial robot (van Wynsberghe 2015 ).

When technologies are used, they always help to shape the context in which they fulfil their function. They help to shape human actions and perceptions, and create new practices and ways of living. Latour ( 1999 ) calls this phenomenon “technological mediation”: technologies mediate the experiences and practices of their users. Such mediations have at least as much moral relevance as technological risks and disaster prevention (Ihde 1999 ; Verbeek 2006 ). Technologies help to shape the quality of our lives and, more importantly, they help to shape our moral actions and decisions. Robots for care can help medical staff in healthcare settings, for example, by monitoring vital signs and thus preventing certain situations in critical paediatric patients, or by personalizing and adjusting therapies with autistic children. However, some studies have warned us that the introduction of telemedicine devices (technologies that are often built into AI robots) has changed healthcare practices and knowledge (Mort et al 2003 ). The heterogeneity of care, as well as robot heterogeneity, implies that the practices and values of care are transformed when new nodes are introduced to healthcare networks. When a robot is introduced to a network of care relations in a hospital, it and the network are transformed. Technologies enable certain relationships between human beings and the world that would not otherwise be possible. However, technologies are not neutral intermediaries, but active mediators that contribute to the formation of human perceptions and interpretations of reality (Verbeek 2006 ). Robots mediate the way we understand and practice care processes, just as the robot is reconstructed from the assemblage of social relations in which it participates (Law and Mol 1995 ).

Following the idea that, in the use of technologies, forms of mediation are performed that are pre-inscribed in the artefact—what Peter-Paul Verbeek ( 2006 ) called the materialized morality— , we analysed how children performed when designing a robot for care (Vallès-Peris et al. 2018 ). Children’s representations of well-being in healthcare settings typically include the presence of their relatives or other people. Their representation of themselves alone while hospitalized or sick is a sad one, while that of being surrounded by relatives, siblings or medical personnel is a happy one. Prospective interactions with a robot that produce a feeling of well-being are imagined in a network with other people. In the same manner, assessment of a robot made by those responsible for care in a hospital (in this case, the focus groups of nurses and volunteers who evaluated the risks and benefits of a dinosaur-shaped robot introduced to the hospital in a pilot phase) is based on the robot’s capacity to be integrated into the relationship network that accompanies the child during his or her hospitalization.

Going beyond the conceptualization of human–robot relationships in a binary model represented by the HRI, the interest of the Robot Embedded in a Network approach (REN) lies in the uncertainties and controversies that appear in daily care in children’s hospitals, such as: how tasks are distributed between nurses and robots, for example, if the nurse is accompanied by a robot that takes the patient's vital signs; how a hospital integrates the psychology staff’s opinions when deciding whether to take part in a pilot program to introduce this type of device to therapies for children with autism; how nurses take advantage of the presence of a robot to entertain a child when trying to insert an IV (intravenous line); how parents and doctors can assess whether it is necessary to collect facial expression data to monitor a child when in the ICU, etc. Knowing how to manage and solve these uncertainties requires alternative ethical frameworks that go beyond “big” philosophical issues about robotics for care; issues that, in turn, are based on “hypothetical” developments of robots for care, not on “real” functionalities and tasks that they can perform nowadays.

5 From an ethical general framework to the context of use, using the ethics of care

This conceptualization of technological innovation raises questions about the traditional ethical approach to AI while delving deeper into the relational theory in bioethics that includes a more-than-human-approach (Lupton 2020 ). Relational theory reshapes the notion of autonomy, emphasizing the patients’ social and contextual circumstances, highlighting that people are always part of social networks and all ethical considerations have to take such networks of relationships into account (Sherwin and Stockdale 2017 ). Deborah Lupton ( 2020 ) proposes consideration of the role of technological artefacts in such networks for a better understanding of digital health and bioethical considerations. In her view, this more-than-human analysis highlights the complexities involved when robots for care or other digital health technologies are introduced to medical settings (Lupton 2020 ). In this same line of thought, we situate ethical and social controversies around care robotics in the network made up by a diverse range of actors: technology designers, healthcare professionals, relatives, patients and robots. Our focus would not be on AI technologies, but on identifying the relevant variables that shape healthcare relationships when AI technologies are introduced.

To develop an ethical framework, together with the STS tradition, we use the notion of the ethics of care. From the different approaches to this field, our proposal is based on the perspective developed by Joan C. Tronto that seeks to understand care from political philosophy. The starting point of Tronto’s approach is that the core definition of the human is its relational involvement with others, in a network of relationships in which each individual has to reconcile different forms of care responsibilities (Vallès-Peris and Domènech 2020 ). In these relationships, the morality of care is bound to concrete situations rather than being abstract and based on principles (Cockburn 2005 ). From these bases, analysis of care processes provides us with a useful guide for thinking about how we perform a particular care task and its ethical dimensions (Tronto 1998 ). Likewise, and in line with the idea that not all collectives face uncertainty under the same conditions, the perspective of the ethics of care engages with those who have difficulties voicing their concerns (Puig de la Bellacasa 2011 ). In medical AI and robotics, this also means identifying prevailing care issues in technological development, what power relations the artefacts generate and what relations they contribute to.

Care experiences and practices can be identified, researched and understood concretely and empirically. However, “care” is ambivalent in its meaning and ontology (Puig de la Bellacasa 2017 ). Assuming this complexity and diversity in its understanding, Fisher and Tronto ( 1990 ) propose it should be viewed from a heterogeneous perspective, inseparable from the economic, political, symbolic and material considerations that shape it. For them, care includes: the practices of care, what is usually considered domestic work; the affections and emotional meaning involved in care; and the organisational and political conception that involves managing and regulating everything that sustains care relationships. In STS focused on health technologies, this perspective has been included in the notion of “empirical ethics of care” (Vallès-Peris 2021a ), from which the analysis of care relationships implies rejecting the logic according to which there is prior and true knowledge about how care should be provided. Thus, the empirical ethics of care revolve around the idea that it is located in the practices of people who, with the help of processes, protocols, routines or machines, act to achieve good care (Willems and Pols 2010 ).

From the idea of heterogeneity, the emergence of any artefact has to do with the diverse biases, values or political and economic relations existing during its creation and design process, conditions that are inscribed in that artefact (Bijker 2009 ; Callon 1998 ). Hence, the creation and design of care robots cannot be disassociated from the neoliberal logic in which we live, from the low social and political value attributed to care, nor from the sexual division of labor that organizes care in an unequal way between women and men (Tronto 2018 ). It is in a similar vein that Maibaum et al. ( 2021 ) propose that robotics should be approached from its political reality. It is commonplace to say that we are living a care crisis, which has to do with the pressure caused by the lack of nurses, teachers, carers and domestic staff, aggravated by demographic change and the ageing of the population (Vallès-Peris et al. 2021b ). In the neoliberal model, the solution to the so-called care crisis is articulated by the market: after years of state downsizing promoted precisely by this neoliberal model, the solution proposed is that the market should be set up to meet human needs (Tronto 2018 ).

In this context, the growth of robotics in the field of healthcare cannot be separated from its powerful economic impact on the technological innovation market. Despite the fact that the care crisis is one of the arguments that are most often used to explain the need to develop care robotics (Maibaum et al. 2021 ), the issue of care tends to be virtually absent from the debates surrounding it—although there are some exceptional proposals, such as those by van Wynsberghe ( 2013 ). This issue can be seen, for example, in the scarce attention given to the tensions that might arise between commercial or business interests and issues linked to the needs and organization of care, in the various guidelines and regulatory mechanisms for AI systems, including robots (Hagendorff 2020 ).

From this approach, it is understood that the ethical debate surrounding AI and robotics cannot focus solely on major philosophical issues, but must also consider aspects linked to healthcare management and everyday practices. Thus, in the case of care robots, the interest should not only lie in questions such as whether it is appropriate to replace humans with robots (Sharkey 2008), whether it is desirable to establish affective bonds (Sharkey and Sharkey 2012 ; Sparrow and Sparrow 2006 ), or liability in the event of harm or damage (Matsuzaki and Lindemann 2016 ). The debate from the ethics of care also looks at identifying the practices, fears and hopes that shape care relationships when robots are introduced to specific contexts and situations. For example, how a nurse in a pediatric ward gets the children in the different rooms to go out into the corridor to feed the pet robot; or how it might be decided at a meeting that from now on, therapies with autistic children will use robots that have been donated by a large robotics company.

From a REN approach, we turn away from the standard discussion about utopian or dystopian scenarios based on the risks and benefits of promised care robots, an approach that seems addressed at building trust and anticipation of objections and reticence (Nordmann and Rip 2009 ). The possibilities for mediation with regard to care robots are configured by the technical elements of a concrete AI device in its interaction with a network of healthcare relations. Thus, it is in that network of social relations that the robot mediates; specific mediations with a particular device within a particular social context (Feng and Feenberg 2008 ). Andrew Feenberg ( 1999 ) illustrates this idea in his instrumentalization theory. The technical dispositions of a robot for care determine the conditions of the robot’s functional possibilities in the network, and these have to do with its design and production history. However, these features are reoriented when they are integrated into a given environment. Robots have their (instrumental) rationality, but when they enter a children’s hospital, what they are and the mediations they make possible depend on their use in that hospital, and in a particular Care Unit with specific protocols and actors (Feenberg 2010 ).

Next to the door to the operating theatre, a mother strokes a robot pet that her child was holding 10 min before and that calms her down while her child is being operated, it is then when the robot becomes a robot for care for the mother. In that situation, controversies and ethical discussions arise about, for example, the dangers of establishing emotional ties with artificial objects. It is in the specific daily practices of different actors involved in healthcare relations in a hospital (patients, relatives, medical staff, volunteers, protocols, units, tests, etc.) that the problems and the rules to solve such problems emerge. Thus, the point of reference of our research is not the robot, but the relationships in which it participates. There is no prominent actor in analysis of and reflection on ethical and social controversies, but rather various actors that are involved in healthcare relations in a children’s hospital.

It is in the collective network of care relations in a particular environment where we situate research on social and ethical issues on medical AI, approaching the topic from a notion close to the empirical ethics of care in which theory around “good care” is localized in practices, and not just underlying them or guiding actions (Willems and Pols 2010 ). Consequently, the results of this type of approach are not prescriptive solutions. They do not give answers to questions such as whether or not to install a robot with a webcam in order to monitor paediatric patients in the ICU at all times but to offer suggestive proposals in relation to specific problems. Continuing with the example, what are the arguments for and against installing a webcam, what are the views of relatives, patients, medical staff, innovation departments, etc., and how can we tweak the robot to fit routines, needs and concerns in this context? (Vallès-Peris and Domènech 2020 ).

6 Caring in the in-between

The heuristics of research on ethical and social issues around care robots, from the three defined movements, could be summarized in three assumptions: (1) if a robot is a network of actors and processes making up an assembly, then the identification of such actors and processes, as well as knowledge of their relations and forms of negotiation, becomes the starting point of any ethical reflection. (2) Since the robot is not only the artefact, but is inscribed within a full network of care, the ethical implications are specific to each context of application, to the way in which the robot changes or modifies the articulation of specific care relationships. (3) Therefore, a responsible framework guiding the introduction of robots to hospitals or other healthcare settings must be particularly linked to these contexts and their ethical principles and values.

As explained in the previous sections, if our conception of robots from the ethics of care assumes heterogeneity, the focus of ethics shifts to the healthcare relationships that are interactively constituted when a robot for care is introduced, and debates on how to introduce responsible robots come to revolve around REN. From a REN perspective, the focus of the analysis is on the network of care relationships in which the robot participates, not on the specific functionalities or characteristics of a robot taken as an isolated entity, nor in the dyadic interaction between individual entities. The network of relationships between multiple agents (the robot, the child, family members, hospital protocols, healthcare staff, etc.) constitutes the only framework for discussion. It is within this framework of particular and specific everyday practices, imaginaries, narratives, etc., that the problems and rules for solving these problems emerge.

From this approach, for example, it is possible to analyze how the introduction of certain robotic devices can change the care relationship between the hospital and the patient, or how the diagnosis or treatment skills of healthcare staff are transformed (Vallès-Peris et al. 2021a ). Focusing attention on REN, we can identify those elements that are considered necessary to ensure that the introduction of a robot does not undermine the ethical values that shape medical practice and care relationships in a particular context. So, it will not be the same to introduce a robot to make a remote diagnostic visit to a child in hospital, while the doctor is elsewhere, as it will be to perform the same visit while the patient is at home. Similarly, it will not be the same when the same robot is used in the hospital to make a follow-up visit to a pediatric patient admitted to the trauma unit, as it is to a patient admitted to the mental health unit.

6.1 An ethical–political proposal to ensure responsible introduction of AI and robotics to healthcare

Focusing on the multiple possibilities for action that emerge in the realm of uncertainty, we propose an ethical and responsible framework focused on care actions in between fears and hopes. From this idea, we take the notion of measured action from an ethics of care. According to Callon et al ( 2009 ), measured action occurs through three interrelated dimensions: (1) a surveillance system: precaution is only possible when formalised socio-technical devices are used to collect information and thereby enable us to move from surveillance to alarm; (2) the deepening of knowledge, through the exploration and identification of excesses. Precaution requires a preliminary assessment of the associated risks and dangers in order to evaluate their severity and (3) the choice of temporary measures to be taken, which must be adapted to each of the situations to which they are applied, guaranteeing their follow-up and update according to the constant polemics that arise. In each of these three dimensions, there may be specific actors, with particular modes of action and different levels of responsibility. Measured action replaces clear and forceful decisions with a series of “small” decisions in each of the three dimensions. Small decisions represent gradual advances, but none of them entails irrevocable commitments.

We adapt the measured action proposed from the ethics of care to discuss care robots and thereby define a responsible framework for introducing these technologies based on “caring in the in-between”. The primary argument regarding the ethics of care is that in order for interdependent living to be conceivable, care is the common element that makes the relationships we establish between our lives and our environment possible. Thus, if we take care seriously, it represents a necessary ethical and political proposition for thinking about the technologies that surround us. This idea problematises a deontological approach according to which ethical standards can only be realised through the declaration of principles. These principles describe moral aspirations, but do not make it possible to think critically or to develop transformative practices (Tronto and Kohlen 2018 ). Thus, responsible robotics, from its conceptualisation as REN, requires institutional changes to the framework in which robotic care is organised, managed and practised. From the integration of measured action and the ethics of care, caring in the in-between is defined as a triadic process or relationship: (a) between the nodes, in the threads of the web; actions as movements of care relations and care processes in which healthcare technologies participate; (b) between the different actors in a network; actions as an exercise of listening to the voices and different concerns of all the actors involved in healthcare settings and integrating their formulations of problems and solutions into the ethical debate; and (c) between fears and hopes, in the realm of uncertainty; actions as a tool for overcoming the polarization between the promises and the dangers of medical AI, thus offering an alternative to abysmal uncertainty.

This proposal focuses on the possibilities for mediation inscribed in the robot, configured by the design of a specific AI device in its interaction with a network of healthcare relations. Thus, broadening and complexifying the process of responsible development of care robots, caring in the in-between is a complement to other proposals that focus on the design process of the artefacts that also integrate the ethics of care -such as the Care Centered Value-Sensitive Design proposed by van Wynsberghe ( 2013 ). From these bases, caring in the in-between is a proposal of responsible robotics that implies institutional challenges, as well as new practices in healthcare systems. It is articulated around three simultaneous processes, each of them related to practical actions in one of the in-between dimensions considered: monitoring relations and caring processes; considering stakeholders’ opinions; and making fears and hopes commensurable. Each of these processes could be defined as:

Monitoring relations and caring processes The creation of local public health systems to monitor the design and introduction of medical AI technologies. This implies the creation of follow-up and assessment procedures to also be applied to pilot or experimental projects. Care is carried out by “all the hospital”, by the networks of material, semiotic and social fluxes that occur every day among all the actors in which technologies participate. If care relations and care practices are placed at the centre of the debate, and the whole hospital is in charge of giving care, then we need to put mechanisms in place that encompass hospitals and other healthcare settings, mechanisms that ensure that the introduction of these devices responds to medical and care values and priorities and not, for example, to commercial or prestige interests. This process of monitoring relations and care processes particularly needs public entities to ensure that the introduction of AI and robotic systems to healthcare settings responds to collective health and well-being needs; and to guarantee that market or innovation interests in AI and robotics are not detrimental to the ethical and social criteria governing public health systems.

Considering stakeholders’ opinions The development of inclusiveness and public participation strategies to establish a prioritisation strategy for the development of IA in health, as well as to identify the main concerns around its introduction. In view of this need, multiple strategies are traditionally developed from STS and what is known as the “participatory turn” in science and technology (hybrid forums, citizen conferences, etc.). The idea of heterogeneity is central to these types of proposals, using diverse mechanisms to integrate the knowledge and expertise of multiple actors (engineers, medical practitioners, formal and informal caregivers, patients or relatives). But it is not enough to speak of heterogeneous assemblies, because in these assemblies not all the agents involved are the same, nor do they participate in the same way, nor are uncertainties distributed equally. Different groups are not equal in their capacity to impose their logic and some groups represent more than others the dominant economic-instrumental interpretation of care inscribed in technology (Hergesell and Maibaum 2018 ). The limitations of participatory processes to address these inequalities are well-known, so there is a need to develop complementary strategies to allow for alternative forms of inclusion and representation in matters related to the introduction of AI and robotics systems to healthcare. In this sense, we propose the need to explore systematic methods to integrate informal and spontaneous popular movements and expression of the population’s fears and hopes (also viewed as potential patients, relatives, and caregivers of the health system).

Making fears and hopes commensurable The choice of progressive, small actions to “keep under control” the effects or consequences of a development, small actions based on progressive feedback loops with monitoring systems and constant debate with all the stakeholders of each hospital or healthcare setting. Such feedback loops will ensure that the introduction of AI devices is in line with care procedures in a specific hospital or healthcare setting and integrates the concerns of all the different actors involved. Undoubtedly, the introduction of forms of feed-back loops also implies slowing down the processes of designing and implementing AI systems in healthcare. We understand that a slowdown is necessary as a prerequisite for radically integrating responsiveness into the design of healthcare technologies.

From this perspective, then, the responsibility to introduce robotic and AI devices in accordance with ethical criteria and societal needs and priorities does not only refer to the technical design process of such devices, nor is it limited to establishing strong legal frameworks to regulate their use. The development of robotics and AI in consideration of ethical and social concerns requires public engagement and institutional changes to health systems on the path towards more responsible technologies. Monitoring relations and caring processes, considering the opinions of stakeholders and making fears and hopes commensurable are, as a whole, a way to introduce democratic mechanisms to technological development. And, it is no doubt impossible to talk about responsible AI and robotics if technology and democracy do not go together. It is also impossible to talk about democracy in AI and robotics if care is not integrated as a central ethical and political proposition in all processes and relations involved in the development of healthcare technologies, what we call “caring in the in-between”.

7 Conclusions

Just as a single technology cannot undertake all care or cure alone, studies of AI and robotics in healthcare cannot be focused only on a particular device. In this paper, we develop an approach grounded on the uncertainty surrounding AI and robotics, on the heterogeneity and mediations that the robot makes possible (what we call the REN approach) and on the ethical and social debate regarding its context of use. Within this framework, we propose a method for responsible introduction of AI and robotics based on an ethical–political proposal called “Caring in the In-Between.” This proposal for action looks into the eyes of uncertainties to collectively discuss ways to design and use technological devices in healthcare settings, while respecting the values that guide public health practice and its networks of care. Using the ethics of care and the notion of measured action developed from the STS, we propose responsible AI and robotics by focusing on the mediations of concrete devices in their interaction with a network of healthcare relations. We articulate AI and responsible robotics around the action that occurs in between fears and hopes, and which revolve around three dimensions of action: (a) caring as monitoring the relations that occur when introducing AI and robotic systems to healthcare settings, through the creation of local public health systems for the purpose of monitoring; (b) caring as including concerns and priorities of the most distanced collectives from the creation and design of knowledge and technologies, with the organization of participatory processes and alternative forms of stakeholder representation; and (c) caring as making fears and hopes commensurable, through the choice of progressive and reversible actions.

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Vallès-Peris, N., Domènech, M. Caring in the in-between: a proposal to introduce responsible AI and robotics to healthcare. AI & Soc 38 , 1685–1695 (2023). https://doi.org/10.1007/s00146-021-01330-w

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Design and piloting of a proposal for intervention with educational robotics for the development of lexical relationships in early childhood education

• Verónica Moreno Campos   ORCID: orcid.org/0000-0002-9700-1135 1 &
• Francisco José Rodríguez Muñoz 2  

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An applied research proposal for integrated learning based on the use of educational robotics has been proposed. The design has been implemented with a sample of 21 four-year-old students applying twenty-first-century competencies (collaboration, creativity, critical thinking, and communication) to learn the curricula related to the development of lexical relations. This research aims to apply data directly derived from the application of educational robotics in the classroom. The research aims focus on two fundamental questions: on the one hand, to verify whether the use of educational robotics in teaching practice is related to greater conceptual achievement and, on the other hand, to validate whether students apply transversal competencies through educational robotics. The results allow us to affirm that the didactic application of robotics activities achieves a high degree of conceptual integration when establishing lexical relationships and allows students to put into practice key transversal problem-solving and critical thinking competencies.

Introduction

Twenty-first-century society can be defined as a culturally connected society due to technology. It is logical that in the field of education, more and more studies are interested in the integration of STEM disciplines (science, technology, engineering, and mathematics) as a way of acquiring what have been called twenty-first-century competencies: collaboration, creativity, critical thinking, and communication (Hussein et al., 2019 ). In a world where technology is present in all aspects of everyday life, teaching must be updated and implement new forms of competency learning where students are not limited to memorizing content but can learn the concepts thanks to the application of new technologies (Barrera Lombana, 2015 ; Anwar et al., 2019 ; Sánchez-Tendero et al., 2019 ; Turan & Aydogdu, 2020 ). In this sense, one of the most critical challenges facing the educational community is precisely knowing how to integrate educational robotics (hereafter, ER) into the design of classroom activities (Nikolopoulou & Gialamas, 2015 ; Papadakis, 2020 ).

ER is an emerging field in education characterized by the use of robots as learning tools capable of linking the different areas of the school curriculum with action competence learning. ER is conceived not only as an end in itself but as a way of enabling problem-based learning, where students must collaborate to solve challenges; in this way, their cognitive and communicative skills are enhanced around the meaningful learning of curricular content.

Most studies are framed within the constructivist paradigm (Piaget, 1968 ), according to which learning is constructed through processes of conceptual assimilation and accommodation through problem-solving. This paper accepts the constructivist statement as a starting point. However, it also integrates the precepts of the cognitive paradigm in which language is understood as another cognitive faculty that interacts with the other cognitive processes (i.e., memory or attention) by establishing a vehicular relationship. The communicative utterances of children when solving problems are important elements in determining how knowledge is acquired (Bruner, 1984 ). Another relevant notion is the fact that learning must be meaningful and transferable. In order for students to integrate knowledge into cognitive frameworks, we must allow them to experience and apply such learning to contexts that replicate or assimilate as much as possible to problems of everyday reality. Only in this way will the acquisition of so-called learning and transfer skills be achieved (Anwar et al., 2019 ).

However, one of the current limitations to integrating ER in the classroom is the lack of teacher training on the didactic possibilities of robotics (Canfarotta & Casado-Muñoz, 2019 ; Gökçearslan et al., 2018 ; Uğur-Erdoğmuş, 2021 ). Without a sound theoretical background and a foundation on which to base the didactic implementation of ER, the cognitive and pedagogical benefits derived from the application of robotics as an educational tool (Angeli & Valanides, 2020 ; García-Valcárcel & Caballero-González, 2019 ; Sullivan & Bers, 2016 ) will not be transferred to students. It is, therefore, urgent that ER research offers models of applied learning to the school curriculum that teachers can understand and apply (Acosta, 2016 ). It is, therefore, necessary that the type of learning be determined based on both the type of learner (infant, primary, secondary, high school, university) and the robot used (Jung & Won, 2018 ).

One of the skills pointed out by studies in robotics is the ability to divide the challenge into smaller tasks, the so-called sequential thinking, which, in the field of language learning, finds its methodological correlate in the task-based approach (Long & Crookes, 1992 ). As stated Langacker ( 1987 ), the notion of profiling plays a fundamental role in this skill wherein the application in ER would explain the fact that, in order to solve the challenge posed, students must be able to select from among all the stimuli received, those that are informatively relevant. Thus, knowledge is acquired by inferring its procedural utility by bringing various interdisciplinary skills into play. This type of integrated learning must be developed from the early stages of education when the child establishes his or her cognitive process. One learning approach that has been successfully implemented with kindergarten students is Robotics Project-based Learning (Papadakis, 2020 ). This proposal includes the approach of knowledge from interdisciplinary areas in its didactic method. With regard to the Spanish curriculum, Sánchez-Tendero et al., ( 2019 ) evaluated the motivation and enjoyment of students in the third year of kindergarten, and their degree of assimilation of a learning process included in the curriculum of the “Knowledge and Interaction with the Environment” area, using the Bee-Bot and Blue-Bot robots. The results indicate that using robotics as a means of learning is both useful and motivating. The benefits of ER in teaching processes have also been found in inclusive education. Likewise, the study by Hamzad et al. ( 2014 ) focused on the teaching process of preschool children with autism. They concluded that, through the use of ER, students managed to better generalize learning by facing challenges appropriate to their level that reproduced contexts closer to their reality than when learning was solely presented by visual or auditory means. Furthermore, the systematic review supported by Tlili et al. ( 2020 ) analyzed the design, implementation, and outcome of robot-assisted in special education research through the perspective of activity theory . This research underlines the importance of designing didactic activities by selecting objectives and robots appropriate to the possibilities and needs of the students.

Our proposal is framed within the inclusive school paradigm advocated by Daniela and Lytras ( 2019 ), where ER is conceived “as a tool for knowledge construction and as an assistive tool for students who have problems in specific fields, or ER may be used to change students' attitudes to learning-class culture-allowing everyone to be accepted and involved” (p. 222). We defend that ER is a motivating learning medium for students, who activate all their competence and communicative strategies to overcome the challenge posed by the teacher. In our case, we present an activity design in which students need to apply their knowledge of the semantic relations corresponding to the second year of Infant Education of the Spanish school curriculum called “Languages: Communication and Representation.” This area includes the linguistic items corresponding to the development and acquisition of 4-year-old children. Our proposal focuses on the semantic component, specifically, the categorical relationships established between concepts. When learning the concepts within our daily environment, inclusive categories are established that organize knowledge according to their similarities, thus obtaining semantic fields. By organizing meanings into semantic fields or hyperonyms (“a lexical unit, an umbrella term, that includes within it, the meaning of other words”), cognitive processing is optimized, since the categories are grouped around a common semantic feature (“a minimal semantic feature, a distinctive component of meaning that differentiates one lexical unit from another”). This cognitive saving, called semantic priming , has been investigated in the area of linguistic development, where several neuroimaging studies on cognitive processing have shown that, when a hyperonym appears in a text, it directly preactivates the related concepts: the hyponyms (Kandahai & Federmeier, 2008 ; Takashima et al., 2019 ; Mathur et al., 2020 ; Luchkina & Waxman, 2021 ). This categorical relationship between concepts is developed and established in the oral language of children between three and four years of age (Mueller & Cramer, 2001 ; Tomasello, 2003 ), wherein the child interacts with his environment and learns the relationship between the representational elements of the world. Thus, in the case of the hyperonym “animal,” the child groups the concepts related to the semas “+alive,” “−human,” “−vegetable” and links the concepts of “cat, lion, elephant…,” which in this case would be the co-hyponyms (“hyponyms that refer to the same hypernym or superordinate term”).

The approach of this study is innovative in that it presents an applied proposal that uses ER as a means of linguistic curricular learning integrated into teaching practice and where not only are learning outcomes measured, but also the viability of the didactic design. In the context of Spain, we found few proposals applied in Early Childhood Education courses focused on the area of “Communication and Representation of Reality.” The proposal by Hidalgo and Pérez-Marín ( 2019 ), whose objective is based on learning to exchange turns of speech in students aged 3–5 years, stands out. However, the methodology and results are not aimed at verifying whether learning had been achieved but rather verify whether the students enjoyed participating and were engaged with the didactic experience.

This research aims to apply data directly derived from the application of educational robotics in the classroom. Our research focuses on the shortcomings identified by Toh et al. ( 2016 ), Jung & Won ( 2018 ), and Hussein ( 2019 ) regarding ER research. It provides actual data to answer the following questions: (1) what knowledge has implemented educational intervention mediated by the use of ER, (2) what didactic objectives related to the curriculum can be utilized in a crosscutting manner thanks to the use of ER, and (3) to which characteristics do the young children's learning processes respond?

Design of the educational intervention proposal

Curricular framework.

Our proposal is framed within the regulations governing the curriculum of Early Childhood Education in Spain ( Real Decreto 95/2022, de 1 de febrero , por el que se establece la ordenación y las enseñanzas mínimas de la Educación Infantil [Royal Decree 95/2022, of February 1, which establishes the organization and minimum teaching of Early Childhood Education] ). The contents are included in Area 3, “Communication and Representation of Reality,” and contribute to the development of communicative competence by focusing on teaching and learning the lexical relationships between the meanings of words.

Ten activities were planned: five for the initial assessment and five for the final assessment. All activities follow the same scheme: Each child is individually presented with a hyperonym and must select the related hyperonym from three images. Only one of the three images is the correct answer (the one that shares the sema with the hyperonym). Of the remaining two responses, one shows some related seme, while the other does not share characteristics or semes. Figure  1 presents an example where the child must relate the hyperonym “furniture” with its hyponym “closet.” As distractors, the image of “television” (which shares the seme “homey”) and the semantically unrelated image “cat” have been included:

Association activity for the hyperonym “furniture”—hyponym “cabinet”

The educational robotics activity (ER) was carried out with a group. Each group then listened to a story narrated by the examiner and then help the protagonist of the story (the robot) to match a hyperonym, such as “means of transportation,” with its hyponym, “bicycle,” from the images represented on the board (Fig.  2 ):

Clementoni’s board used for the ER activity

Materials and methods

Participants.

The study involved 21 children enrolled in a public nursery school in the city of Valencia (Spain): specifically, 11 girls and 10 boys between the ages of 4 and 5 (M  = 4.5). All participants lived with their families and had a middle socioeconomic and cultural level. The students' legal guardians signed an informed consent form authorizing their children to participate in the activity.

Instruments

The initial and final knowledge assessment was carried out by selecting 15 × 25 cm images on a white background and without graphic aids. For the development of the ER activity, we used the material provided was by Super.Doc from the Clementoni publishing house: It contains a cardboard puzzle board with printed images and a robot without parts measuring 41.8 × 9.3 × 27.8 cm and 1.84 kg whose programming only allows spatial movements in a straight line of 15 cm and with the possibility of programming 45-degree turns (Fig.  3 ).

Robot and control panel

The TEPI scale ( Toy Effects on Play Instrument , Trawick-Smith et al., 2011 ) was applied to qualitatively assess the degree of individual achievement of the objectives related to the construction of learning and the so-called twenty-first-century competencies: thinking and learning (knowledge construction, problem-solving) creativity, imagination, social collaboration, and independent use.

We followed the guidelines endorsed by Tejada ( 2005 ) and Mayor ( 2008 ) for the assessment of educational intervention programs according to the relationship between the purpose of the evaluation and the moment of learning, namely: initial assessment of prior knowledge, assessment of the development of the intervention proposal (Hervás et al., 2018 ), and final assessment of learning outcomes (Causo et al., 2015 ).

In the initial evaluation, the student's previous knowledge of the lexical relationship of words was assessed. The purpose of this evaluation was to lay the foundation for measuring the effectiveness of the intervention after analyzing the learning outcomes. To this end, five hyperonyms were selected, and each student was asked to select the related hyponym from a series of three images. Each series was composed of a semantically unrelated word, the correct answer, and a related word, either by analogy or in opposition (e. g., hyperonym “vehicle,” options: “stone,” “car,” “horse”). The number of successes was quantified, but also the type of failures. The teacher conducted the initial assessment in a 40 min session, and data collection was done on an Excel spreadsheet.

A multilevel analysis was carried out to assess the development of the intervention proposal. The methodological possibilities of the activity were initially analyzed, followed by an analysis of the didactic applications of the proposal (Hervás et al., 2018 ).

Methodological possibilities: applicability of the design, possibility of teamwork, and problem-solving capacity.

Didactic possibilities: ability to communicate among group members, cooperative work, and ability to design and plan the solution to the challenge posed.

The proposed intervention was carried out in two 40 min sessions. In the first session, three groups were assessed, and, in the second, four groups consisting of three randomly distributed students, but with the condition that they were constituted under the premise of sexual heterogeneity. Each evaluation was conducted in a room from the rest of the classmates to avoid interference. The researcher narrated a motor story where they had to help the robot solve five challenges to find the compass that would help him return to his planet. The five challenges were based on creating associations between a hyponym and its hyperonym, or vice versa. For example, the researcher posed: “the robot must catch its animal,” and the children had to associate the hyperonym with the image of the hyponym that appeared on the board: in this instance, “cat.” Once the semic relationship was found, the group had to arrange the movement arrows to organize the robot's movement, program it, and solve the next challenge.

In the final assessment, included in Table 1 , the group successes were quantified. The nature of the failures was qualitatively assessed according to whether they were due to difficulties in establishing the seme relationship (related/unrelated) or to difficulties in programming the robot.

The initial assessment aims to establish the students' knowledge base and analyze the difficulties in learning and consolidating inclusive lexical relationships. After the initial test, the average number of successes was 2.8. The highest percentage of errors pertained to choosing unrelated words (63%), while 27% identified incorrect concepts but with a semantic linkage.

When assessing the ER activity, the methodological and didactic possibilities of the approach were assessed through the TEPI scale (Trawick-Smith et al., 2011 ). Researchers qualitatively assessed the degree of performance of each student on a 5-point Likert scale based on twenty-first-century competencies:

Thinking and Learning:

Constructing Knowledge: how students manifest the acquisition and assimilation of new knowledge through action in the proposed activity.

Problem-solving: students show a variety of resources when solving the challenges posed.

Inquiry: expressing interest and asking questions to satisfy the needs of the activity.

Engagement: students show interest throughout the activity.

Creativity and Imagination:

Creative Expression: use and diversity of oral expression (verbal and nonverbal).

Imagination: how each child manifests creative thinking.

Social Interaction and Independent Use:

Collaboration: how children manifest intergroup collaborative behaviors.

Independent Use: how children demonstrate independent skills without peer or adult assistance.

Appendix 1 shows the TEPI scale results of each student's performance during the robotic activity. The examiners rated from 1 to 5 the degree of competency integration shown by each student. In the Thinking and Learning section, the averages obtained in Inquiry (3.95), Constructing Knowledge (3.76), and Engagement (3.67) stand out because they allow us to infer the degree of attention and, therefore, the conceptual use of the activity. In the Creativity and Imagination area, the mean score in Imagination (3.38) is higher than that of Creative Expression (2.95). This result could be primarily explained by the tendency to reproduce peers' expressions when working in a group. In terms of Social Interaction and Independent Use, the children showed a high degree of predisposition to collaborate with each other (3.9). However, when it came to showing individual robot programming skills, some children required the adult's help, so the mean score was lower than the rest of the scores (3.5).

In the final evaluation, each child was again asked to make five associations between a hyperonym and its corresponding hyponym, replicating the initial assessment. Tables 2 , 3 and 4 compare the success and failure typology before and after the robotic activity. Statistical analyses were performed using the t -test carried out with the SPSS software.

In 71.42% of cases, students made fewer errors in lexical relations after the educational activity with robotics: They went from 56.19% correct results in the initial activity to 75.24% in the final assessment. In addition, students made fewer unrelated errors regarding the type of errors committed (Tables 3 , 4 ), and the decrease in unrelated errors was significant. When students selected the incorrect concept after the robotics activity, they tended to select the semantically related concept. The qualitative analysis shows that, in the initial evaluation, the errors not semantically related to the hyperonym comprised 17.14% of the incorrect answers, while in the final assessment, they decreased to 6.67%. Errors due to selecting a related answer utilizing a seme have been significant: They have gone from representing 25.71% in the initial assessment to 17.14% in the final assessment. These results suggest that the students have integrated, for the most part, the mechanisms of semantic categorization and lexical relation.

An applied proposal for integrated learning based on the use of educational robotics has been proposed in which 21 four-year-old students applied twenty-first-century competencies (collaboration, creativity, critical thinking, and communication) to achieve curricular learning corresponding to lexical relations, under the heading of “Communication and Representation of Reality.” The study design took into account the issues identified by Tlili et al. ( 2020 ) on choosing an appropriate robot according to the age of the students and the design of activities in which ER could serve as a didactic strategy for the achievement of a goal appropriate to the needs of the students. In this case, we selected the Super.Doc robot and set a didactic objective so that the ER-based activity would help internalize an important curricular content in the process of language acquisition and development.

To assess the conclusions of our study, we answer the questions we posed at the beginning of the research:

What knowledge has implemented educational intervention mediated by the use of educational robotics?

The learning results have been significant in all cases. These results suggest that the benefits of using the robotics-based activity are centered on meaningful learning and the designed learning context that facilitates students' understanding of the lexical relationships established between concepts. In the pretest activity, an explanation was given as to how the concepts were related to each other. Students were limited to considering the possible lexical relationships between the selection presented. However, in the ER activity, students practiced different cognitive and communicative skills to solve the challenge posed. This challenge was located in a concrete situational context, which undoubtedly helped students internalize the semantic relationships between concepts. This contextual and related learning explains why students produced fewer response failures in the posttests. Furthermore, when examining the typology of errors, it was found that the number of semantically unrelated responses decreased significantly in the posttest phase, and for the most part, they were become semantically related errors, indicating that students, for the most part, understood how lexical relations are established.

What didactic objectives related to the curriculum can be utilized in a crosscutting manner thanks to educational robotics?

Thanks to ER, all the classroom competencies and curriculum content can be applied in an integrated manner. The design of applied activities enables students to work in groups to solve challenges and, in this way, develop transversal skills such as critical thinking and leadership abilities. In our proposal, we assessed the degree of performance shown by each student in three categories of the so-called twenty-first-century competencies: Thinking and Learning, Creativity and Imagination, and Social Interaction and Independent Use.

The category where students scored the highest was Thinking and Learning . The scores obtained by the students in Inquiry and Constructing Knowledge categories reflect the high degree of interest and motivation engendered by the ER-based activity, which subsequently translated into a high degree of conceptual assimilation. The Problem-Solving category obtained the lowest scores in the category, which could be explained by the sexually heterogeneous composition of the working groups. As pointed out in the study by Sun et al. ( 2022 ), male students show more applied thinking, and female students show more skills in communication and selection of the most effective solution.

In the Creativity and Imagination category, the score in Imagination stands out. The group work arrangement enabled students to be creative in devising solutions to challenges. However, this same arrangement determines the score in the Expressive Communication category since the linguistically more capable students who take on the role of communicative leaders monopolize the speaking turns of the other group members. Finally, in the Social Interaction and Independent Use category, the students showed a high degree of intergroup collaboration. However, when programming the robot, they showed little familiarity with robotics as a regular classroom teaching tool and requested the adult's help to program the robot.

What are the characteristics of young children's learning process?

If we analyze the results of our study, two important factors have been highlighted that should be considered when planning didactic proposals with ER. The first factor refers to the curricular design of the knowledge; specifically, the applicability of the contents to a real context where students can transfer knowledge from conceptual abstraction to its internalization through practical application must be assessed. The second factor is related to the importance of the ER activity in activating the crosscutting cognitive and linguistic skills that are part of the so-called twenty-first century.

There are still some limitations to the implementation of this study. Firstly, the time for carrying out the activity was limited by the school's schedule and calendar. It could have been possible to better assess the students' performance and internalization of the strategies and contents after an intervention with more sessions, but this was not possible. Secondly, although the researchers conducted the ER-based activity, both the initial and the final activities were assessed by teachers as the teachers did not consider themselves sufficiently trained in ER to carry out the activity. In addition, the examiners' access to the educational center was restricted due to COVID-19 restrictive measures. Third, the sample size of our study just allows us to describe a trend in favor of using ER as a powerful resource to achieve learning outcomes. Nevertheless, our study paves the way for future research that, with a larger number of participants, can not only better reflect the effects of our language learning activities, but also establish, in a constructivist frame, a firm link between the pedagogical use of ER and its learning benefits, always oriented toward the development of dynamic and strategic skills working in a problem-based learning environment. Finally, this study has not considered the influence of variables such as gender, level of oral proficiency, or previous level of lexical knowledge. Future research should consider different variables and unify the data collection and evaluation process to be performed by researchers.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the OSF Registries repository Data: osf.io/m6u9e.

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Acknowledgements

The authors would like to thank the parents of the students who participated in the study, as well as the public school where the activity took place.

This research received no specific Grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Verónica Moreno Campos

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PhD. VM-C designed the linguistic activity, established contact with the school and obtained parental consent for the study to be carried out. Subsequently, she collected the data and participated in the writing of the scientific article. PhD. FJ participated in the design of the language activity and analyzed the data collected, in addition to writing the scientific article. Both authors read and approved the final version of the manuscript.

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Verónica Moreno Campos is a lecturer at the Faculty of Human Sciences in Jaume I University. She studied Hispanic Philology and Speech Therapy degrees. She completed his training with a PhD in Developmental and Educational Psychology. Her doctoral thesis was awarded the extraordinary prize of the University of Valencia. Her current research relates to the relationship between learning disabilities and new forms of teaching and learning, such as ICTs. She is member of GREAT research group (Teaching, Learning & TEchnology) and IP of TECNEDI (Educative Technology applied to Didactics).

José Francisco Rodríguez Muñoz holds a PhD in Applied Linguistics and is Professor of Didactics of Language and Literature at the University of Almería (Spain). Among his main lines of research are pragmatics and language learning. His papers have appeared in international journals such as PLOS ONE, L1-Educational Studies in Language and Literature, Pragmatics (IPrA), Spanish Journal of Applied Linguistics (SJAL), Research in Language, Psychology of Language and Communication, etc. He has recently authored some publications about the use of ICT educational resources for learning language and literature, such as “The digital competence of secondary school literature teachers in Spain” (Texto Livre, 2021), in the frame of the project “Reading in the digital age: new reading practices, participatory culture and affinity spaces” (EDU2015-69924-R).

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Campos, V.M., Muñoz, F.J.R. Design and piloting of a proposal for intervention with educational robotics for the development of lexical relationships in early childhood education. Smart Learn. Environ. 10 , 6 (2023). https://doi.org/10.1186/s40561-023-00226-0

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Research proposal on computing visual computing and robotics, 1.0 introduction.

The advancement of the Internet and cloud computing has pushed the desktop-based peripheral device into a web-based Computing Infrastructure . It has transformed the actual goods or items principle into facilities. The support and dedication of several governments and major tech companies around the world to create and use cloud computing environments as an interconnected storage and connectivity system have contributed to the rapid growth of various commercial and mission-critical technologies in the modern architecture, including the Introduction of physical devices, which provides the Internet of Things has unlimited computing capabilities. Internet of Things (IoT) was initially suggested and adhered in the Radio Frequency Identification RFID-tags to commemorate the Electronic Product Code (Auto-ID Lab). The IoT idea is expanded to the globe where physical artifacts are deeply integrated into the information system and where physical artifacts could become actively involved in business operations. The Internet of Intelligent Things (IoIT) interacts with smart devices with sufficient computing capability. The IoIT is an aspect of dispersed intelligence (Du Plessis et al., 2015).

As per Intel’s study, there are 15 billion systems connected to the Internet, 4 billion of which have the 32-bit processing power, and 1 billion of those are smart systems (Steenman, 2012). The Autonomous Decentralized System (ADS) is a distributed system consisting of components or materials intended to function separately but competent of communicating with each other to meet the overall objective of the system. The ADS components are intended to function in a composable manner and the data is shared via a content-oriented protocol. This architecture model allows the machine to continue to operate in the case of device failures. It also allows servicing and restoration to be carried out whilst the machine is in service. ADS and associated developments have a broad variety of uses in automotive production lines, train signalling and robotics (Mori, 2008). ADS principles became the basis for subsequent developments like cloud storage and the Internet of Things. Robot as a Service (RaaS) is a cloud computing platform that enables the seamless incorporation of robots and integrated devices into network and cloud computing environments (Chen et al., 2010; Chen & Zhou, 2015).

Despite service-oriented architecture (SOA), the RaaS framework comprises configuration tools, the exploration software registry and the user’s direct access application clients (Chen & Tsai, 2012). The new RaaS architecture enables SOAP and RESTful connectivity between RaaS and other cloud storage systems. Hardware-software and specifications are required to facilitate the introduction of RaaS. For instance, Devices Profile for Web Services (DPWS) specifies deployment restrictions that require safe Web Service messaging, discovery, definition, and eventing on resource-restricted software between Web services and applications. The latest Intel IoT-enabled software, such as Galileo and Edison, has made it possible to program such tools as cloud services. Through various viewpoints, the RaaS machine may be called a device of the Internet of Things (IoT), the Internet of Intelligent Things (IoIT) with a sufficient processing power to conduct complex computing (Chen & Hu, 2013), the Cyberphysical System (CPS) which is a mixture of a broad computational and connectivity centre and physical elements which can communicate with the physical world (Steenman, 2012), as well as an autonomous generation.

2.0 Problem Statement

Present contests in robotics are primarily remote controlled. Students spend nearly all their time constructing a Mechanical Computer with no technical need, as technical an autonomous robot is beyond the capacity of high school students (Harvey et al., 1992).

3.0 Aim and Objective

The aim is to develop an easy-to-build and easy-to-program robotic package for the distribution of high schools and the development of a competition sequence of autonomous robots.

4.0 Literature Review

This section is focused on our earlier work on service-oriented robotics Computing Science . In, Chen (2006) introduced the core concept of service- robotics computing and the initial development of the design and modules utilizing Parallax Boe- and Windows CE- handheld tools. The study was funded by the Embedded System Program of Microsoft Science. A current version of the service- robotics computing architecture and design was recorded in Chen and Bai, (2008) and Chen et al.,(2009). The latest design is carried out using an Intel processor-based self-built robot and off-the-shelflf parts. The emphasis of (Chen & Bai, 2008) was on the coordination of robots, the design of interfaces between sensors/actuators as well as the processor board, and assistance for service-oriented computing. The emphasis of (Chen et al., 2008) was on event-driven design, floor-detection algorithms, office patrol algorithms, and simulation of algorithm performance tests. In (Chen et al., 2009), commented on the efficiency assessment of the execution time required for a defined collection of tasks and the power consumption centred on the implementation of the Intel Core 2 Duo processor.

4.1 Research Gap

From the above Research Gap , as IoT / robotics technology and developments grow unpredictably into other fields of computing, knowledge and control systems, schools and colleges should train students to learn and be enabled to program IoT devices and robots. However, programming IoT and physical devices are challenging and relies on a clear knowledge of hardware as well as low-level programming. To fix this problem, workflow, as well as visual programming languages, need to be created.

5.0 Methodology

The main contribution of this paper is the definition of Robot as a Service (RaaS) that enforces the design and execution of a robot or computer to be an all-in-one SOA package, that is, a package that involves software output systems, discovery and publishing data brokers, and client direct control applications. In our previous SOA robot concept, the robot is an interface which uses remote backend computing resources. This all-in-one architecture gives the robot machine even more strength and ability to perform as a completely self-contained computer platform in the cloud computing world. Another main contribution recorded in the paper is the creation of facilities which convert the Microsoft Robotics Studio VPL (Visual Programming Language) software into Intel application executable files. These resources require the creation of standard VPL systems on an Intel-based robotic platform.

To illustrate the ideas, we have placed in position a RaaS prototype. To render the RaaS more adaptable, we made the following design decisions: Hardware: standardized Intel processor and motherboard are used. We’ve been checking the RaaS on Core 2 Owing and Atom processors. The key part list includes:

In this research, a combination of the novel machine learning algorithm is proposed using image processing, test processing and deep learning techniques. The contribution of this work is as follows: Intel Core 2 Duo Processor 1.6GHz; alternatively, Atom N270 processor 1.6GHz;

• Arduino Board
• MD23 Dual Motor Driver
• M2-ATX Intelligent Power Supply
• USB to I2C Communications Module;
• Intel Embedded Mini-ITX Motherboard;

Generic USB and popular serial port tools, like sonar sensors, compass sensors, motion sensors and thermal sensors, webcams, remote servos and motors, are often used. Figure 1 illustrates how the systems can be controlled by utilities and drivers.

Figure 1 Interfacing devices to SOA

Operating systems: We have incorporated a variant of Windows XP and a variant of Linux.Software development Languages: We utilized C # and Java to program the systems and applications. We have also introduced a module that interfaces the Visual Programming Language (VPL) applications to the Intel network.

Service Hosting: Using multithreading in C # and Java, we introduced a multi-tenant software hosting system where different copies of the application code are hosted by a worker machine. The thread-based database hosting system That requests are handled by the reactor as a single entry point in which the requests are cached and reviewed against the archive. If an item or service is not recorded, the demand would be denied automatically. Otherwise, the request will be sent to the hosting employee process. The worker cycle should launch the project as a thread and control the existence of the project.  We have two systems for jobs, one for services and one for application.

6.0 Summary

This paper described the idea of Robot as a Service (RaaS) using VPL (Visual Programming Language) computing, addressed the implementation of the RaaS prototype. The results often demonstrate the efficacy of the software and hardware device supporting the RaaS complex machine. A robotics starter kit is being produced l for distribution to high school robotics camps and robotics competitions. Many programs are still being examined in future.

• Chen, Y. (2006). Service-oriented computing in recomposable embedded systems. In: Joint IARP/IEEE-RAS/EURON/IFIP 10.4 Workshop on Dependability in Robotics and Autonomous Systems . [Online]. 2006, pp. 15–19. Available from: http://webhost.laas.fr/TSF/IFIPWG/Workshops&Meetings/49/workshop/04 chen.pdf.
• Chen, Y., Abhyankar, S., Xu, L., Tsai, W.-T. & Garcia-Acosta, M. (2008). Developing a security robot in service-oriented architecture. In: 2008 12th IEEE International Workshop on Future Trends of Distributed Computing Systems . [Online]. 2008, IEEE, pp. 106–111. Available from: https://ieeexplore.ieee.org/abstract/document/4683122/.
• Chen, Y. & Bai, X. (2008). On robotics applications in service-oriented architecture. In: 2008 The 28th International Conference on Distributed Computing Systems Workshops . [Online]. 2008, IEEE, pp. 551–556. Available from: https://ieeexplore.ieee.org/abstract/document/4577843/.
• Chen, Y., Du, Z. & Garcia-Acosta, M. (2010). Robot as a service in cloud computing. In: 2010 Fifth IEEE International Symposium on Service Oriented System Engineering . [Online]. 2010, IEEE, pp. 151–158. Available from: https://ieeexplore.ieee.org/abstract/document/5570010/.
• Chen, Y. & Hu, H. (2013). Internet of intelligent things and robot as a service. Simulation Modelling Practice and Theory . 34. pp. 159–171.
• Chen, Y., Sabnis, A. & Garcia-Acosta, M. (2009). Design and performance evaluation of a service-oriented robotics application. In: 2009 29th IEEE International Conference on Distributed Computing Systems Workshops . [Online]. 2009, IEEE, pp. 292–299. Available from: https://ieeexplore.ieee.org/abstract/document/5158868/.
• Chen, Y. & Tsai, W. (2012). Service-Oriented Computing and Web Software Integration: From Principles to Development . [Online]. Kendall Hunt Publishing Company. Available from: https://books.google.co.in/books?id=gFyGMAEACAAJ.
• Chen, Y. & Zhou, Z. (2015). Robot as a service in computing curriculum. In: 2015 IEEE Twelfth International Symposium on Autonomous Decentralized Systems . [Online]. 2015, IEEE, pp. 156–161. Available from: https://ieeexplore.ieee.org/abstract/document/7098252/.
• Harvey, I., Husbands, P. & Cliff, D. (1992). Issues in evolutionary robotics . [Online]. School of Cognitive and Computing Sciences, University of Sussex. Available from: https://pdfs.semanticscholar.org/36e5/64d1338129b38cede11e99c0b5d8bb2d9cd9.pdf.
• Mori, K. (2008). Autonomous decentralized system and its strategic approach for research and development. IEICE transactions on information and systems . [Online]. 91 (9). pp. 2227–2232. Available from: https://www.jstage.jst.go.jp/article/transinf/E91.D/9/E91.D_9_2227/_article/-char/ja/.
• Du Plessis, M., Wakelin, Z. & Nel, P. (2015). The influence of emotional intelligence and trust on servant leadership. SA Journal of Industrial Psychology . [Online]. 41 (1). Available from: https://www.sciencedirect.com/science/article/pii/S1569190X12000469.
• Steenman, T. (2012). Accelerating the transition to intelligent systems. In: Intel Embedded Research and Education Summit .

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Automated machine learning robot unlocks new potential for genetics research

This technology will save labs time and money while enabling large-scale experiments.

University of Minnesota Twin Cities researchers have constructed a robot that uses machine learning to fully automate a complicated microinjection process used in genetic research.

In their experiments, the researchers were able to use this automated robot to manipulate the genetics of multicellular organisms, including fruit fly and zebrafish embryos. The technology will save labs time and money while enabling them to more easily conduct new, large-scale genetic experiments that were not possible previously using manual techniques

The research is featured on the cover of the April 2024 issue of GENETICS , a peer-reviewed, open access, scientific journal. The work was co-led by two University of Minnesota mechanical engineering graduate students Andrew Alegria and Amey Joshi. The team is also working to commercialize this technology to make it widely available through the University of Minnesota start-up company, Objective Biotechnology.

Microinjection is a method for introducing cells, genetic material, or other agents directly into embryos, cells, or tissues using a very fine pipette. The researchers have trained the robot to detect embryos that are one-hundredth the size of a grain of rice. After detection, the machine can calculate a path and automate the process of the injections.

"This new process is more robust and reproducible than manual injections," said Suhasa Kodandaramaiah, a University of Minnesota mechanical engineering associate professor and senior author of the study. "With this model, individual laboratories will be able to think of new experiments that you couldn't do without this type of technology."

Typically, this type of research requires highly skilled technicians to perform the microinjection, which many laboratories do not have. This new technology could expand the ability to perform large experiments in labs, while reducing time and costs.

"This is very exciting for the world of genetics. Writing and reading DNA have drastically improved in recent years, but having this technology will increase our ability to perform large-scale genetic experiments in a wide range of organisms," said Daryl Gohl, a co-author of the study, the group leader of the University of Minnesota Genomics Center's Innovation Lab and research assistant professor in the Department of Genetics, Cell Biology, and Development.

Not only can this technology be used in genetic experiments, but it can also help to preserve endangered species through cryopreservation, a preservation technique conducted at ultra-low temperatures.

"You can use this robot to inject nanoparticles into cells and tissues that helps in cryopreservation and in the process of rewarming afterwards," Kodandaramaiah explained.

Other team members highlighted other applications for the technology that could have even more impact.

"We hope that this technology could eventually be used for in vitro fertilization, where you could detect those eggs on the microscale level," said Andrew Alegria, co-lead author on the paper and University of Minnesota mechanical engineering graduate research assistant in the Biosensing and Biorobotics Lab.

• Medical Devices
• Personalized Medicine
• Medical Imaging
• Educational Technology
• Artificial Intelligence
• Robot calibration
• Computational neuroscience
• Industrial robot
• Molecular biology
• Weight machine
• Computer vision

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Journal Reference :

• Andrew D Alegria, Amey S Joshi, Jorge Blanco Mendana, Kanav Khosla, Kieran T Smith, Benjamin Auch, Margaret Donovan, John Bischof, Daryl M Gohl, Suhasa B Kodandaramaiah. High-throughput genetic manipulation of multicellular organisms using a machine-vision guided embryonic microinjection robot . GENETICS , 2024; 226 (4) DOI: 10.1093/genetics/iyae025

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Dear Colleague Letter: Joint National Science Foundation and United States Department of Agriculture National Institute of Food and Agriculture Funding Opportunity: Supporting Foundational Research in Robotics (FRR)

April 18, 2024

Dear Colleague:

Recognizing the importance of use-inspired collaborations in promoting scientific discoveries, the National Science Foundation (NSF), in collaboration with United States Department of Agriculture National Institute of Food and Agriculture (USDA/NIFA), seeks proposals to advance foundational research in agricultural robotics. These proposals should be of mutual interest to the NSF Foundational Research in Robotics (FRR) program and to USDA/NIFA .

NSF's FRR program, jointly led by the Directorate for Engineering (ENG) and the Directorate for Computer and Information Science and Engineering (CISE), supports research to create innovative robots with unprecedented new functionality. USDA/NIFA has the mission to provide leadership and funding for programs that advance agriculture-related sciences. Proposals submitted under this Dear Colleague Letter (DCL) should present a compelling vision for pioneering robots with transformative potential in agricultural contexts. It is highly suggested that potential proposers contact the USDA/NIFA program director first (listed below) with a short narrative to determine project applicability for this program. If appropriate, an NSF program director will be further consulted.

PROPOSAL SUBMISSION REQUIREMENTS

NSF is the lead agency for this collaboration. Proposals to be considered under this Dear Colleague Letter should have a title prefixed by "NIFA:" and should be submitted to the FRR program. Submissions will be evaluated in FRR review panels, following the requirements of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) ( https://new.nsf.gov/policies/pappg ), and the FRR Program Description ( https://new.nsf.gov/funding/opportunities/foundational-research-robotics-frr ). Proposals submitted under this Dear Colleague Letter must be clearly justified by important needs in agriculture and the agricultural sciences.

NSF will manage and conduct the review process of proposals submitted in accordance with NSF standards and procedures, as described in the PAPPG. USDA staff will participate in panels as observers during the discussion of USDA-focused proposals. Information about proposals and unattributed reviews of proposals will be shared with USDA staff. NSF and NIFA will meet as soon as possible after the proposals have been reviewed to formulate a set of funding recommendations consistent with the goals of this DCL. Note that if a proposal is selected for an award to be funded by NIFA, NSF will request the submitting institution withdraw their NSF proposal and submit to NIFA.

Recipients funded by NIFA will be encouraged to participate in annual FRR grantee meetings, along with recipients funded by NSF.

Interested parties are encouraged to contact the listed program directors at NSF and USDA/NIFA prior to submission.

TECHNICAL POINTS OF CONTACT

FRR Program Officers:

USDA/NIFA Program Officers:

Margaret Martonosi Assistant Director Directorate for Computer and Information Science and Engineering<

Susan Margulies Assistant Director Directorate for Engineering

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NSF and USDA join forces to boost innovation in agricultural robotics

The U.S. National Science Foundation and the U.S. Department of Agriculture's National Institute of Food and Agriculture (USDA NIFA) are teaming up to advance foundational research in agricultural robotics. The agencies are issuing a new Dear Colleague Letter to solicit visionary research proposals to develop robots with the potential to revolutionize farming practices.

The collaboration stems from a shared recognition of the critical role that robotics can play in addressing challenges in agriculture and food production, such as increased demand for food and the need for precision agriculture practices. By leveraging resources from both agencies, NSF and USDA seek to foster interdisciplinary research that will tackle agricultural challenges and increase sustainability.

"This partnership represents a unique opportunity to harness the power of robotics to address pressing challenges in agriculture," said Michael Littman, director for the NSF Division of Information and Intelligent Systems. "By bringing together researchers from diverse backgrounds, we can unlock new insights and develop cutting-edge solutions that will benefit farmers and consumers alike."

Daniel Linzell, director of the NSF Division of Civil, Mechanical and Manufacturing Innovation added, "This new collaboration between NIFA and NSF underscores the value of our long-standing partnership and our commitment to foundational robotics research for the agriculture sector. Our joint investment and the community’s ideas will not only make agriculture safer for the people who grow our food but also will lessen harmful impacts on the planet that sustains us."

Steven Thomson, National Program Leader with USDA NIFA, highlighted the importance of addressing agricultural challenges through innovative technologies. "Agricultural robotics holds tremendous promise for enhancing productivity, reducing environmental impact and improving the overall resilience of our food systems. Through this collaboration, we aim to catalyze breakthroughs that will benefit farmers, consumers and the environment."

Under the joint funding opportunity, proposals will be solicited to support research projects that align with the goals of both the NSF Foundational Research in Robotics program and USDA NIFA. Proposals submitted under this initiative will undergo rigorous evaluation by both agencies.

• Learn more about this opportunity, please see the NSF Dear Colleague Letter.

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Atlas, a Humanoid Robot From Boston Dynamics, Is Leaping Into Retirement

It has been replaced by a new model, which will be used in automotive manufacturing. A farewell video featured the old machine running outdoors, performing back flips and awkwardly shimmying.

By Johnny Diaz

Atlas, the humanoid robot that dazzled followers for more than a decade with its outdoor running, awkward dancing and acrobatic back flips, has powered down. In other words, it is retiring.

On Wednesday, Boston Dynamics, the company that created it, announced the arrival of the next generation of humanoid robots — a fully electric robot (also named Atlas) for real-world commercial and industrial applications.

For anyone worried about what would happen to the hydraulic bipedal machine (a robot home? the junkyard? a window display?) that was created for research purposes, the company had an answer. A spokesman, Nikolas Noel, said that retirement would mean that the Atlas would move to its “robot retirement home,” which is to say that it would be “sitting in our office lobby museum” with other decommissioned robots.

The old Atlas was used to research full-body mobility and to explore what was possible in robotics, Mr. Noel said. It was not designed for commercial use and was first developed as part of a competition to further the use of robots “in future natural and man-made disasters,” according to the Defense Advanced Research Projects Agency of the Pentagon.

“For almost a decade, Atlas has sparked our imagination, inspired the next generations of roboticists and leapt over technical barriers in the field,” Boston Dynamics said in a farewell video posted on social media on Tuesday.

“Now it’s time for our hydraulic Atlas robot to kick back and relax,” the company said.

The company’s farewell video captured the brawny 6-foot-2 machine in action over the years. That included taking a stroll in a grassy field, leaping on boxes (or picking up 10-pound ones), carefully walking on a rock bed and awkwardly shimmying.

But the video also featured some mishaps, including the robot’s frequent stumbles such as falling over on platforms, rolling down a hill and leaking hydraulic fluid from its leg inside a lab.

The new model has a big round head that spins completely around, is leaner and can nimbly rise from a horizontal position to a bipedal stance in seconds. Its hips appear to be reversible, so it might be better than us at some yoga poses.

The company’s commercial models include Spot, an agile four-legged robot, and Stretch, an elongated warehouse platform.

“The new Atlas builds on decades of research and furthers our commitment to delivering the most capable, useful mobile robots solving the toughest challenges in industry today: with Spot, with Stretch, and now with Atlas,” the company wrote in a video post introducing the new robot .

The new model will be used to build “the next generation of automotive manufacturing capabilities” with Hyundai Motor Company, which owns Boston Dynamics.

The original Atlas made its public debut in 2013 in Waltham, Mass., where Boston Dynamics is based, after it received initial funding from the Defense Advanced Research Projects Agency.

The company was awarded a $10.8 million contract to work with the agency on developing Atlas for the D.A.R.P.A. Robotics Challenge.

There were seven updated Atlases, each of which was made from aircraft-grade aluminum and titanium and weighed 330 pounds. They were then used as base models by teams competing for a $2 million prize in the challenge. But the final challenge was won by a Korean team that built a robot that could kneel and roll around on wheels as it performed tasks.

During its training, researchers were tough on the Atlas, even hurling weights at it to see how well it responded and adapted to challenges inside and outside the lab.

Johnny Diaz is a general assignment reporter covering breaking news. He previously worked for the South Florida Sun Sentinel and The Boston Globe. More about Johnny Diaz

Hamas Says It Received Israel's Response to Its Ceasefire Proposal

Hamas Says It Received Israel's Response to Its Ceasefire Proposal

A chair is left in front of posters with pictures of hostages, who were kidnapped during the deadly October 7 attack on Israel by Palestinian Islamist group Hamas, amid the ongoing conflict in Gaza between Israel and Hamas, in Tel Aviv, Israel, April 26, 2024. REUTERS/Shannon Stapleton

By Nidal al-Mughrabi

CAIRO (Reuters) - Hamas said it had received on Saturday Israel's official response to its latest ceasefire proposal and will study it before submitting its reply, the group's deputy Gaza chief said in a statement.

"Hamas has received today the official response of the Zionist occupation to the proposal presented to the Egyptian and the Qatari mediators on April 13," Khalil Al-Hayya, who is currently based in Qatar, said in a statement published by the group.

After more than six months of war with Israel in Gaza, the negotiations remain deadlocked, with Hamas sticking to its demands that any agreement must end the war.

War in Israel and Gaza

An Egyptian delegation visited Israel for discussion with Israeli officials on Friday, looking for a way to restart talks to end the conflict and return remaining hostages taken when Hamas fighters stormed into Israeli towns on Oct. 7, an official briefed on the meetings said.

The official, who spoke on condition of anonymity, said Israel had no new proposals to make, although it was willing to consider a limited truce in which 33 hostages would be released by Hamas, instead of the 40 previously under discussion.

On Thursday, the United States and 17 other countries appealed to Hamas to release all of its hostages as a pathway to end the crisis.

Hamas has vowed not to relent to international pressure but in a statement it issued on Friday it said it was "open to any ideas or proposals that take into account the needs and rights of our people".

However, it stuck to its key demands that Israel has rejected, and criticised the joint statement issued by the U.S and others for not calling for a permanent ceasefire and the withdrawal of Israeli forces from Gaza.

White House national security adviser Jake Sullivan said on Friday he saw fresh momentum in talks to end the war and return the remaining hostages.

Citing two Israeli officials, Axios reported that Israel told the Egyptian mediators on Friday that it was ready to give hostage negotiations "one last chance" to reach a deal with Hamas before moving forward with an invasion of Rafah, the last refuge for around a million Palestinians who fled Israeli forces further north in Gaza earlier in the war.

Meanwhile, in Rafah, Palestinian health officials said an Israeli air strike on a house killed at least five people and wounded others.

Hamas fighters stormed into Israeli towns on Oct. 7, killing 1,200 people and capturing 253 hostages. Israel has sworn to annihilate Hamas in an onslaught that has killed more than 34,000 Palestinians.

(Reporting by Nidal al-Mughrabi; Writing by Ahmed Tolba and Nidal al-Mughrabi; Editing by Sandra Maler and Rosalba O'Brien)

Copyright 2024 Thomson Reuters .

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