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Title: foundation models in robotics: applications, challenges, and the future.

Abstract: We survey applications of pretrained foundation models in robotics. Traditional deep learning models in robotics are trained on small datasets tailored for specific tasks, which limits their adaptability across diverse applications. In contrast, foundation models pretrained on internet-scale data appear to have superior generalization capabilities, and in some instances display an emergent ability to find zero-shot solutions to problems that are not present in the training data. Foundation models may hold the potential to enhance various components of the robot autonomy stack, from perception to decision-making and control. For example, large language models can generate code or provide common sense reasoning, while vision-language models enable open-vocabulary visual recognition. However, significant open research challenges remain, particularly around the scarcity of robot-relevant training data, safety guarantees and uncertainty quantification, and real-time execution. In this survey, we study recent papers that have used or built foundation models to solve robotics problems. We explore how foundation models contribute to improving robot capabilities in the domains of perception, decision-making, and control. We discuss the challenges hindering the adoption of foundation models in robot autonomy and provide opportunities and potential pathways for future advancements. The GitHub project corresponding to this paper (Preliminary release. We are committed to further enhancing and updating this work to ensure its quality and relevance) can be found here: this https URL

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• Published: 17 July 2024

The need for reproducible research in soft robotics

• Robert Baines   ORCID: orcid.org/0000-0002-9023-1536 1 ,
• Dylan Shah 2 ,
• Jeremy Marvel 3 ,
• Jennifer Case 4 &
• Andrew Spielberg 5  

Nature Machine Intelligence volume  6 ,  pages 740–741 ( 2024 ) Cite this article

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• Mechanical engineering
• Research management

Recent years have witnessed the rise of commercialization efforts for soft robotics technology, which includes soft grippers, stretchable sensors and platforms for human–robot interactions. However, this commercialization lags behind the trends seen with other robotics technologies at equivalent points in their respective lifecycles. For example, the first patent for an industrial robotic manipulator was filed in 1954, and within two decades, robotic manipulators were adopted onto assembly lines across the world 1 . By comparison, despite their origins in the 1980s and an influx of publications starting around 2004, soft robotics technologies are scarce in society 2 . This deployment gap is due largely to uncertainties surrounding the absence of standards, as well as difficulties in replicating published solutions.

Research comprises a dynamic interplay between discovery and distillation into practice. So far in soft robotics, novelty presides. Little emphasis has been placed on rigorous comparisons across studies, and consequently soft robotics lacks standard benchmarks, metrics, data sets, measurement and characterization workflows, and manufacturing recipes. These challenges can be seen across scales.

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Robert Baines

Hardware Research and Development, Arieca Inc., Pittsburgh, PA, USA

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Accelerator Complex Technology Division, Fermilab, Batavia, IL, USA

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Baines, R., Shah, D., Marvel, J. et al. The need for reproducible research in soft robotics. Nat Mach Intell 6 , 740–741 (2024). https://doi.org/10.1038/s42256-024-00869-9

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Trends and research foci of robotics-based STEM education: a systematic review from diverse angles based on the technology-based learning model

• Darmawansah Darmawansah   ORCID: orcid.org/0000-0002-3464-4598 1 ,
• Gwo-Jen Hwang   ORCID: orcid.org/0000-0001-5155-276X 1 , 3 ,
• Mei-Rong Alice Chen   ORCID: orcid.org/0000-0003-2722-0401 2 &
• Jia-Cing Liang   ORCID: orcid.org/0000-0002-1134-527X 1  

International Journal of STEM Education volume  10 , Article number:  12 ( 2023 ) Cite this article

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Fostering students’ competence in applying interdisciplinary knowledge to solve problems has been recognized as an important and challenging issue globally. This is why STEM (Science, Technology, Engineering, Mathematics) education has been emphasized at all levels in schools. Meanwhile, the use of robotics has played an important role in STEM learning design. The purpose of this study was to fill a gap in the current review of research on Robotics-based STEM (R-STEM) education by systematically reviewing existing research in this area. This systematic review examined the role of robotics and research trends in STEM education. A total of 39 articles published between 2012 and 2021 were analyzed. The review indicated that R-STEM education studies were mostly conducted in the United States and mainly in K-12 schools. Learner and teacher perceptions were the most popular research focus in these studies which applied robots. LEGO was the most used tool to accomplish the learning objectives. In terms of application, Technology (programming) was the predominant robotics-based STEM discipline in the R-STEM studies. Moreover, project-based learning (PBL) was the most frequently employed learning strategy in robotics-related STEM research. In addition, STEM learning and transferable skills were the most popular educational goals when applying robotics. Based on the findings, several implications and recommendations to researchers and practitioners are proposed.

Introduction

Over the past few years, implementation of STEM (Science, Technology, Engineering, and Mathematics) education has received a positive response from researchers and practitioners alike. According to Chesloff ( 2013 ), the winning point of STEM education is its learning process, which validates that students can use their creativity, collaborative skills, and critical thinking skills. Consequently, STEM education promotes a bridge between learning in authentic real-life scenarios (Erdoğan et al., 2016 ; Kelley & Knowles, 2016 ). This is the greatest challenge facing STEM education. The learning experience and real-life situation might be intangible in some areas due to pre- and in-conditioning such as unfamiliarity with STEM content (Moomaw, 2012 ), unstructured learning activities (Sarama & Clements, 2009), and inadequate preparation of STEM curricula (Conde et al., 2021 ).

In response to these issues, the adoption of robotics in STEM education has been encouraged as part of an innovative and methodological approach to learning (Bargagna et al., 2019 ; Ferreira et al., 2018 ; Kennedy et al., 2015 ; Köse et al., 2015 ). Similarly, recent studies have reported that the use of robots in school settings has an impact on student curiosity (Adams et al., 2011 ), arts and craftwork (Sullivan & Bers, 2016 ), and logic (Bers, 2008 ). When robots and educational robotics are considered a core part of STEM education, it offers the possibility to promote STEM disciplines such as engineering concepts or even interdisciplinary practices (Okita, 2014 ). Anwar et. al. ( 2019 ) argued that integration between robots and STEM learning is important to support STEM learners who do not immediately show interest in STEM disciplines. Learner interest can elicit the development of various skills such as computational thinking, creativity and motivation, collaboration and cooperation, problem-solving, and other higher-order thinking skills (Evripidou et al., 2020 ). To some extent, artificial intelligence (AI) has driven the use of robotics and tools, such as their application to designing instructional activities (Hwang et al., 2020 ). The potential for research on robotics in STEM education can be traced by showing the rapid increase in the number of studies over the past few years. The emphasis is on critically reviewing existing research to determine what prior research already tells us about R-STEM education, what it means, and where it can influence future research. Thus, this study aimed to fill the gap by conducting a systematic review to grasp the potential of R-STEM education.

In terms of providing the core concepts of roles and research trends of R-STEM education, this study explored beyond the scope of previous reviews by conducting content analysis to see the whole picture. To address the following questions, this study analyzed published research in the Web of Science database regarding the technology-based learning model (Lin & Hwang, 2019 ):

In terms of research characteristic and features, what were the location, sample size, duration of intervention, research methods, and research foci of the R-STEM education research?

In terms of interaction between participants and robots, what were the participants, roles of the robot, and types of robot in the R-STEM education research?

In terms of application, what were the dominant STEM disciplines, contribution to STEM disciplines, integration of robots and STEM, pedagogical interventions, and educational objectives of the R-STEM research?

• Literature review

Previous studies have investigated the role of robotics in R-STEM education from several research foci such as the specific robot users (Atman Uslu et al., 2022 ; Benitti, 2012 ; Jung & Won, 2018 ; Spolaôr & Benitti, 2017 ; van den Berghe et al., 2019 ), the potential value of R-STEM education (Çetin & Demircan, 2020 ; Conde et al., 2021 ; Zhang et al., 2021 ), and the types of robots used in learning practices (Belpaeme et al., 2018 ; Çetin & Demircan, 2020 ; Tselegkaridis & Sapounidis, 2021 ). While their findings provided a dynamic perspective on robotics, they failed to contribute to the core concept of promoting R-STEM education. Those previous reviews did not summarize the exemplary practice of employing robots in STEM education. For instance, Spolaôr and Benitti ( 2017 ) concluded that robots could be an auxiliary tool for learning but did not convey whether the purpose of using robots is essential to enhance learning outcomes. At the same time, it is important to address the use and purpose of robotics in STEM learning, the connections between theoretical pedagogy and STEM practice, and the reasons for the lack of quantitative research in the literature to measure student learning outcomes.

First, Benitti ( 2012 ) reviewed research published between 2000 and 2009. This review study aimed to determine the educational potential of using robots in schools and found that it is feasible to use most robots to support the pedagogical process of learning knowledge and skills related to science and mathematics. Five years later, Spolaôr and Benitti ( 2017 ) investigated the use of robots in higher education by employing the adopted-learning theories that were not covered in their previous review in 2012. The study’s content analysis approach synthesized 15 papers from 2002 to 2015 that used robots to support instruction based on fundamental learning theory. The main finding was that project-based learning (PBL) and experiential learning, or so-called hands-on learning, were considered to be the most used theories. Both theories were found to increase learners’ motivation and foster their skills (Behrens et al., 2010 ; Jou et al., 2010 ). However, the vast majority of discussions of the selected reviews emphasized positive outcomes while overlooking negative or mixed outcomes. Along the same lines, Jung and Won ( 2018 ) also reviewed theoretical approaches to Robotics education in 47 studies from 2006 to 2017. Their focused review of studies suggested that the employment of robots in learning should be shifted from technology to pedagogy. This review paper argued to determine student engagement in robotics education, despite disagreements among pedagogical traits. Although Jung and Won ( 2018 ) provided information of teaching approaches applied in robotics education, they did not offer critical discussion on how those approaches were formed between robots and the teaching disciplines.

On the other hand, Conde et. al. ( 2021 ) identified PBL as the most common learning approach in their study by reviewing 54 papers from 2006 to 2019. Furthermore, the studies by Çetin and Demircan ( 2020 ) and Tselegkaridis and Sapounidis ( 2021 ) focused on the types of robots used in STEM education and reviewed 23 and 17 papers, respectively. Again, these studies touted learning engagement as a positive outcome, and disregarded the different perspectives of robot use in educational settings on students’ academic performance and cognition. More recently, a meta-analysis by Zhang et. al. ( 2021 ) focused on the effects of robotics on students’ computational thinking and their attitudes toward STEM learning. In addition, a systematic review by Atman Uslu et. al. ( 2022 ) examined the use of educational robotics and robots in learning.

So far, the review study conducted by Atman Uslu et. al. ( 2022 ) could be the only study that has attempted to clarify some of the criticisms of using educational robots by reviewing the studies published from 2006 to 2019 in terms of their research issues (e.g., interventions, interactions, and perceptions), theoretical models, and the roles of robots in educational settings. However, they failed to take into account several important features of robots in education research, such as thematic subjects and educational objectives, for instance, whether robot-based learning could enhance students’ competence of constructing new knowledge, or whether robots could bring either a motivational facet or creativity to pedagogy to foster students’ learning outcomes. These are essential in investigating the trends of technology-based learning research as well as the role of technology in education as a review study is aimed to offer a comprehensive discussion which derived from various angles and dimensions. Moreover, the role of robots in STEM education was generally ignored in the previous review studies. Hence, there is still a need for a comprehensive understanding of the role of robotics in STEM education and research trends (e.g., research issues, interaction issues, and application issues) so as to provide researchers and practitioners with valuable references. That is, our study can remedy the shortcomings of previous reviews (Additional file 1 ).

The above comments demonstrate how previous scholars have understood what they call “the effectiveness of robotics in STEM education” in terms of innovative educational tools. In other words, despite their useful findings and ongoing recommendations, there has not been a thorough investigation of how robots are widely used from all angles. Furthermore, the results of existing review studies have been less than comprehensive in terms of the potential role of robotics in R-STEM education after taking into account various potential dimensions based on the technology-based model that we propose in this study.

The studies in this review were selected from the literature on the Web of Science, our sole database due to its rigorous journal research and qualified studies (e.g., Huang et al., 2022 ), discussing the adoption of R-STEM education, and the data collection procedures for this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al., 2009 ) as referred to by prior studies (e.g., Chen et al., 2021a , 2021b ; García-Martínez et al., 2020 ). Considering publication quality, previous studies (Fu & Hwang, 2018 ; Martín-Páez et al., 2019 ) suggested using Boolean expressions to search Web of Science databases. The search terms for “robot” are “robot” or “robotics” or “robotics” or “Lego” (Spolaôr & Benitti, 2017 ). According to Martín-Páez et. al. ( 2019 ), expressions for STEM education include “STEM” or “STEM education” or “STEM literacy” or “STEM learning” or “STEM teaching” or “STEM competencies”. These search terms were entered into the WOS database to search only for SSCI papers due to its wide recognition as being high-quality publications in the field of educational technology. As a result, 165 papers were found in the database. The search was then restricted to 2012–2021 as suggested by Hwang and Tsai ( 2011 ). In addition, the number of papers was reduced to 131 by selecting only publications of the “article” type and those written in “English”. Subsequently, we selected the category “education and educational research” which reduced the number to 60 papers. During the coding analysis, the two coders screened out 21 papers unrelated to R-STEM education. The coding result had a Kappa coefficient of 0.8 for both coders (Cohen, 1960 ). After the screening stage, a final total of 39 articles were included in this study, as shown in Fig.  1 . Also, the selected papers are marked with an asterisk in the reference list and are listed in Appendixes 1 and 2 .

PRISMA procedure for the selection process

Theoretical model, data coding, and analysis

This study comprised content analysis using a coding scheme to provide insights into different aspects of the studies in question (Chen et al., 2021a , 2021b ; Martín-Páez et al., 2019 ). The coding scheme adopted the conceptual framework proposed by Lin and Hwang ( 2019 ), comprising “STEM environments”, “learners”, and “robots”, as shown in Fig.  2 . Three issues were identified:

In terms of research issues, five dimensions were included: “location”, “sample size”, “duration of intervention”, (Zhong & Xia, 2020 ) “research methods”, (Johnson & Christensen, 2000 ) and “research foci”. (Hynes et al., 2017 ; Spolaôr & Benitti, 2017 ).

In terms of interaction issues, three dimensions were included: “participants”, (Hwang & Tsai, 2011 ), “roles of the robot”, and “types of robot” (Taylor, 1980 ).

In terms of application, five dimensions were included, namely “dominant STEM disciplines”, “integration of robot and STEM” (Martín‐Páez et al., 2019 ), “contribution to STEM disciplines”, “pedagogical intervention”, (Spolaôr & Benitti, 2017 ) and “educational objectives” (Anwar et al., 2019 ). Table 1 shows the coding items in each dimension of the investigated issues.

Model of R-STEM education theme framework

Figure  3 shows the distribution of the publications selected from 2012 to 2021. The first two publications were found in 2012. From 2014 to 2017, the number of publications steadily increased, with two, three, four, and four publications, respectively. Moreover, R-STEM education has been increasingly discussed within the last 3 years (2018–2020) with six, three, and ten publications, respectively. The global pandemic in the early 2020s could have affected the number of papers published, with only five papers in 2021. This could be due to the fact that most robot-STEM education research is conducted in physical classroom settings.

Number of publications on R-STEM education from 2012 to 2021

Table 2 displays the journals in which the selected papers were published, the number of papers published in each journal, and the journal’s impact factor. It can be concluded that most of the papers on R-STEM education research were published in the Journal of Science Education and Technology , and the International Journal of Technology and Design Education , with six papers, respectively.

Research issues

The geographic distribution of the reviewed studies indicated that more than half of the studies were conducted in the United States (53.8%), while Turkey and China were the location of five and three studies, respectively. Taiwan, Canada, and Italy were indicated to have two studies each. One study each was conducted in Australia, Mexico, and the Netherlands. Figure  4 shows the distribution of the countries where the R-STEM education was conducted.

Locations where the studies were conducted ( N  = 39)

Sample size

Regarding sample size, there were four most common sample sizes for the selected period (2012–2021): greater than 80 people (28.21% or 11 out of 39 studies), between 41 and 60 (25.64% or 10 out of 39 studies), 1 to 20 people (23.08% or 9 out of 39), and between 21 and 40 (20.51% or 8 out of 39 studies). The size of 61 to 80 people (2.56% or 1 out of 39 studies) was the least popular sample size (see Fig.  5 ).

Sample size across the studies ( N  = 39)

Duration of intervention

Regarding the duration of the study (see Fig.  6 ), experiments were mostly conducted for less than or equal to 4 weeks (35.9% or 14 out of 39 studies). This was followed by less than or equal to 8 weeks (25.64% or 10 out of 39 studies), less than or equal to 6 months (20.51% or 8 out 39 studies), less than or equal to 12 months (10.26% or 4 out of 39 studies), while less than or equal to 1 day (7.69% or 3 out of 39 studies) was the least chosen duration.

Duration of interventions across the studies ( N  = 39)

Research methods

Figure  7 demonstrates the trends in research methods from 2012 to 2021. The use of questionnaires or surveys (35.9% or 14 out of 39 studies) and mixed methods research (35.9% or 14 out of 39 studies) outnumbered other methods such as experimental design (25.64% or 10 out of 39 studies) and system development (2.56% or 1 out of 39 studies).

Frequency of each research method used in 2012–2021

Research foci

In these studies, research foci were divided into four aspects: cognition, affective, operational skill, and learning behavior. If the study involved more than one research focus, each issue was coded under each research focus.

In terms of cognitive skills, students’ learning performance was the most frequently measured (15 out of 39 studies). Six studies found that R-STEM education brought a positive result to learning performance. Two studies did not find any significant difference, while five studies showed mixed results or found that it depends. For example, Chang and Chen ( 2020 ) revealed that robots in STEM learning improved students’ cognition such as designing, electronic components, and computer programming.

In terms of affective skills, just over half of the reviewed studies (23 out of 39, 58.97%) addressed the students’ or teachers’ perceptions of employing robots in STEM education, of which 14 studies showed positive perceptions. In contrast, nine studies found mixed results. For instance, Casey et. al. ( 2018 ) determined students’ mixed perceptions of the use of robots in learning coding and programming.

Five studies were identified regarding operational skills by investigating students’ psychomotor aspects such as construction and mechanical elements (Pérez & López, 2019 ; Sullivan & Bers, 2016 ) and building and modeling robots (McDonald & Howell, 2012 ). Three studies found positive results, while two reported mixed results.

In terms of learning behavior, five out of 39 studies measured students’ learning behavior, such as students’ engagement with robots (Ma et al., 2020 ), students’ social behavior while interacting with robots (Konijn & Hoorn, 2020 ), and learner–parent interactions with interactive robots (Phamduy et al., 2017 ). Three studies showed positive results, while two found mixed results or found that it depends (see Table 3 ).

Interaction issues

Participants.

Regarding the educational level of the participants, elementary school students (33.33% or 13 studies) were the most preferred study participants, followed by high school students (15.38% or 6 studies). The data were similar for preschool, junior high school, in-service teachers, and non-designated personnel (10.26% or 4 studies). College students, including pre-service teachers, were the least preferred study participants. Interestingly, some studies involved study participants from more than one educational level. For example, Ucgul and Cagiltay ( 2014 ) conducted experiments with elementary and middle school students, while Chapman et. al. ( 2020 ) investigated the effectiveness of robots with elementary, middle, and high school students. One study exclusively investigated gifted and talented students without reporting their levels of education (Sen et al., 2021 ). Figure  8 shows the frequency of study participants between 2012 and 2021.

Frequency of research participants in the selected period

The roles of robot

For the function of robots in STEM education, as shown in Fig.  9 , more than half of the selected articles used robots as tools (31 out of 39 studies, 79.49%) for which the robots were designed to foster students’ programming ability. For instance, Barker et. al. ( 2014 ) investigated students’ building and programming of robots in hands-on STEM activities. Seven out of 39 studies used robots as tutees (17.95%), with the aim of students and teachers learning to program. For example, Phamduy et. al. ( 2017 ) investigated a robotic fish exhibit to analyze visitors’ experience of controlling and interacting with the robot. The least frequent role was tutor (2.56%), with only one study which programmed the robot to act as tutor or teacher for students (see Fig.  9 ).

Frequency of roles of robots

Types of robot

Furthermore, in terms of the types of robots used in STEM education, the LEGO MINDSTORMS robot was the most used (35.89% or 14 out of 39 studies), while Arduino was the second most used (12.82% or 5 out of 39 studies), and iRobot Create (5.12% or 2 out of 39 studies), and NAO (5.12% or 2 out of 39 studies) ranked third equal, as shown in Fig.  10 . LEGO was used to solve STEM problem-solving tasks such as building bridges (Convertini, 2021 ), robots (Chiang et al., 2020 ), and challenge-specific game boards (Leonard et al., 2018 ). Furthermore, four out of 36 studies did not specify the robots used in their studies.

Frequency of types of robots used

Application issues

The dominant disciplines and the contribution to stem disciplines.

As shown in Table 4 , the most dominant discipline in R-STEM education research published from 2012 to 2021 was technology. Engineering, mathematics, and science were the least dominant disciplines. Programming was the most common subject for robotics contribution to the STEM disciplines (25 out of 36 studies, 64.1%), followed by engineering (12.82%), and mathematical method (12.82%). We found that interdisciplinary was discussed in the selected period, but in relatively small numbers. However, this finding is relevant to expose the use of robotics in STEM disciplines as a whole. For example, Barker et. al. ( 2014 ) studied how robotics instructional modules in geospatial and programming domains could be impacted by fidelity adherence and exposure to the modules. The dominance of STEM subjects based on robotics makes it necessary to study the way robotics and STEM are integrated into the learning process. Therefore, the forms of STEM integration are discussed in the following sub-section to report how teaching and learning of these disciplines can have learning goals in an integrated STEM environment.

Integration of robots and STEM

There are three general forms of STEM integration (see Fig.  11 ). Of these studies, robot-STEM content integration was commonly used (22 studies, 56.41%), in which robot activities had multiple STEM disciplinary learning objectives. For example, Chang and Chen ( 2020 ) employed Arduino in a robotics sailboat curriculum. This curriculum was a cross-disciplinary integration, the objectives of which were understanding sailboats and sensors (Science), the direction of motors and mechanical structures (Engineering), and control programming (Technology). The second most common form was supporting robot-STEM content integration (12 out of 39 studies, 30.76%). For instance, KIBO robots were used in the robotics activities where the mechanical elements content area was meaningfully covered in support of the main programming learning objectives (Sullivan & Bers, 2019 ). The least common form was robot-STEM context integration (5 out of 39 studies, 12.82%) which was implemented through the robot to situate the disciplinary content goals in another discipline’s practices. For example, Christensen et. al. ( 2015 ) analyzed the impact of an after-school program that offered robots as part of students’ challenges in a STEM competition environment (geoscience and programming).

The forms of robot-STEM integration

Pedagogical interventions

In terms of instructional interventions, as shown in Fig.  12 , project-based learning (PBL) was the preferred instructional theory for using robots in R-STEM education (38.46% or 15 out 39 studies), with the aim of motivating students or robot users in the STEM learning activities. For example, Pérez and López ( 2019 ) argued that using low-cost robots in the teaching process increased students’ motivation and interest in STEM areas. Problem-based learning was the second most used intervention in this dimension (17.95% or 7 out of 39 studies). It aimed to improve students’ motivation by giving them an early insight into practical Engineering and Technology. For example, Gomoll et. al. ( 2017 ) employed robots to connect students from two different areas to work collaboratively. Their study showed the importance of robotic engagement in preliminary learning activities. Edutainment (12.82% or 5 out of 39 studies) was the third most used intervention. This intervention was used to bring together students and robots and to promote learning by doing. Christensen et. al. ( 2015 ) and Phamduy et. al. ( 2017 ) were the sample studies that found the benefits of hands-on and active learning engagement; for example, robotics competitions and robotics exhibitions could help retain a positive interest in STEM activities.

The pedagogical interventions in R-STEM education

Educational objectives

As far as the educational objectives of robots are concerned (see Fig.  13 ), the majority of robots are used for learning and transfer skills (58.97% or 23 out of 39 studies) to enhance students’ construction of new knowledge. It emphasized the process of learning through inquiry, exploration, and making cognitive associations with prior knowledge. Chang and Chen’s ( 2020 ) is a sample study on how learning objectives promote students’ ability to transfer science and engineering knowledge learned through science experiments to design a robotics sailboat that could navigate automatically as a novel setting. Moreover, it also explicitly aimed to examine the hands-on learning experience with robots. For example, McDonald and Howell ( 2012 ) described how robots engaged with early year students to better understand the concepts of literacy and numeracy.

Educational objectives of R-STEM education

Creativity and motivation were found to be educational objectives in R-STEM education for seven out of 39 studies (17.94%). It was considered from either the motivational facet of social trend or creativity in pedagogy to improve students’ interest in STEM disciplines. For instance, these studies were driven by the idea that employing robots could develop students’ scientific creativity (Guven et al., 2020 ), confidence and presentation ability (Chiang et al., 2020 ), passion for college and STEM fields (Meyers et al., 2012 ), and career choice (Ayar, 2015 ).

The general benefits of educational robots and the professional development of teachers were equally found in four studies each. The first objective, the general benefits of educational robotics, was to address those studies that found a broad benefit of using robots in STEM education without highlighting the particular focus. The sample studies suggested that robotics in STEM could promote active learning and improve students’ learning experience through social interaction (Hennessy Elliott, 2020 ) and collaborative science projects (Li et al., 2016 ). The latter, teachers’ professional development, was addressed by four studies (10.25%) to utilize robots to enhance teachers’ efficacy. Studies in this category discussed how teachers could examine and identify distinctive instructional approaches with robotics work (Bernstein et al., 2022 ), design meaningful learning instruction (Ryan et al., 2017 ) and lesson materials (Kim et al., 2015 ), and develop more robust cultural responsive self-efficacy (Leonard et al., 2018 ).

This review study was conducted using content analysis from the WOS collection of research on robotics in STEM education from 2012 to 2021. The findings are discussed under the headings of each research question.

RQ 1: In terms of research, what were the location, sample size, duration of intervention, research methods, and research foci of the R-STEM education research?

About half of the studies were conducted in North America (the USA and Canada), while limited studies were found from other continents (Europe and the Asia Pacific). This trend was identified in the previous study on robotics for STEM activities (Conde et al., 2021 ). Among 39 studies, 28 (71.79%) had fewer than 80 participants, while 11 (28.21%) had more than 80 participants. The intervention’s duration across the studies was almost equally divided between less than or equal to a month (17 out of 39 studies, 43.59%) and more than a month (22 out of 39 studies, 56.41%). The rationale behind the most popular durations is that these studies were conducted in classroom experiments and as conditional learning. For example, Kim et. al. ( 2018 ) conducted their experiments in a course offered at a university where it took 3 weeks based on a robotics module.

A total of four different research methodologies were adopted in the studies, the two most popular being mixed methods (35.89%) and questionnaires or surveys (35.89%). Although mixed methods can be daunting and time-consuming to conduct (Kucuk et al., 2013 ), the analysis found that it was one of the most used methods in the published articles, regardless of year. Chang and Chen ( 2022 ) embedded a mixed-methods design in their study to qualitatively answer their second research question. The possible reason for this is that other researchers prefer to use mixed methods as their research design. Their main research question was answered quantitatively, while the second and remaining research questions were reported through qualitative analysis (Casey et al., 2018 ; Chapman et al., 2020 ; Ma et al., 2020 ; Newton et al., 2020 ; Sullivan & Bers, 2019 ). Thus, it was concluded that mixed methods could lead to the best understanding and integration of research questions (Creswell & Clark, 2013 ; Creswell et al., 2003 ).

In contrast, system development was the least used compared to other study designs, as most studies used existing robotic systems. It should be acknowledged that the most common outcome we found was to enable students to understand these concepts as they relate to STEM subjects. Despite the focus on system development, the help of robotics was identified as increasing the success of STEM learning (Benitti, 2012 ). Because limited studies focused on system development as their primary purpose (1 out of 39 studies, 2.56%), needs analyses may ask whether the mechanisms, types, and challenges of robotics are appropriate for learners. Future research will need further design and development of personalized robots to fill this part of the research gap.

About half of the studies (23 studies, 58.97%) were focused on investigating the effectiveness of robots in STEM learning, primarily by collecting students’ and teachers’ opinions. This result is more similar to Belpaeme et al. ( 2018 ) finding that users’ perceptions were common measures in studies on robotics learning. However, identifying perceptions of R-STEM education may not help us understand exactly how robots’ specific features afford STEM learning. Therefore, it is argued that researchers should move beyond such simple collective perceptions in future research. Instead, further studies may compare different robots and their features. For instance, whether robots with multiple sensors, a sensor, or without a sensor could affect students’ cognitive, metacognitive, emotional, and motivational in STEM areas (e.g., Castro et al., 2018 ). Also, there could be instructional strategies embedded in R-STEM education that can lead students to do high-order thinking, such as problem-solving with a decision (Özüorçun & Bicen, 2017 ), self-regulated and self-engagement learning (e.g., Li et al., 2016 ). Researchers may also compare the robotics-based approach with other technology-based approaches (e.g., Han et al., 2015 ; Hsiao et al., 2015 ) in supporting STEM learning.

RQ 2: In terms of interaction, what were the participants, roles of the robots, and types of robots of the R-STEM education research?

The majority of reviewed studies on R-STEM education were conducted with K-12 students (27 studies, 69.23%), including preschool, elementary school, junior, and high school students. There were limited studies that involved higher education students and teachers. This finding is similar to the previous review study (Atman Uslu et al., 2022 ), which found a wide gap among research participants between K-12 students and higher education students, including teachers. Although it is unclear why there were limited studies conducted involving teachers and higher education students, which include pre-service teachers, we are aware of the critical task of designing meaningful R-STEM learning experiences which is likely to require professional development. In this case, both pre- and in-service teachers could examine specific objectives, identify topics, test the application, and design potential instruction to align well with robots in STEM learning (Bernstein et al., 2022 ). Concurrently, these pedagogical content skills in R-STEM disciplines might not be taught in the traditional pre-service teacher education and particular teachers’ development program (Huang et al., 2022 ). Thus, it is recommended that future studies could be conducted to understand whether robots can improve STEM education for higher education students and teachers professionally.

Regarding the role of robots, most were used as learning tools (31 studies, 79.48%). These robots are designed to have the functional ability to command or program some analysis and processing (Taylor, 1980 ). For example, Leonard et. al. ( 2018 ) described how pre-service teachers are trained in robotics activities to facilitate students’ learning of computational thinking. Therefore, robots primarily provide opportunities for learners to construct knowledge and skills. Only one study (2.56%), however, was found to program robots to act as tutors or teachers for students. Designing a robot-assisted system has become common in other fields such as language learning (e.g., Hong et al., 2016 ; Iio et al., 2019 ) and special education (e.g., Özdemir & Karaman, 2017 ) where the robots instruct the learning activities for students. In contrast, R-STEM education has not looked at the robot as a tutor, but has instead focused on learning how to build robots (Konijn & Hoorn, 2020 ). It is argued that robots with features as human tutors, such as providing personalized guidance and feedback, could assist during problem-solving activities (Fournier-Viger et al., 2013 ). Thus, it is worth exploring in what teaching roles the robot will work best as a tutor in STEM education.

When it comes to types of robots, the review found that LEGO dominated robots’ employment in STEM education (15 studies, 38.46%), while the other types were limited in their use. It is considered that LEGO tasks are more often associated with STEM because learners can be more involved in the engineering or technical tasks. Most researchers prefer to use LEGO in their studies (Convertini, 2021 ). Another interesting finding is about the cost of the robots. Although robots are generally inexpensive, some products are particularly low-cost and are commonly available in some regions (Conde et al., 2021 ). Most preferred robots are still considered exclusive learning tools in developing countries and regions. In this case, only one study offered a low-cost robot (Pérez & López, 2019 ). This might be a reason why the selected studies were primarily conducted in the countries and continents where the use of advanced technologies, such as robots, is growing rapidly (see Fig.  4 ). Based on this finding, there is a need for more research on the use of low-cost robots in R-STEM instruction in the least developed areas or regions of the world. For example, Nel et. al. ( 2017 ) designed a STEM program to build and design a robot which exclusively enabling students from low-income household to participate in the R-STEM activities.

RQ 3: In terms of application, what were the dominant STEM disciplines, contribution to STEM disciplines, integration of robots and STEM, pedagogical interventions, and educational objectives of the R-STEM research?

While Technology and Engineering are the dominant disciplines, this review found several studies that directed their research to interdisciplinary issues. The essence of STEM lies in interdisciplinary issues that integrate one discipline into another to create authentic learning (Hansen, 2014 ). This means that some researchers are keen to develop students’ integrated knowledge of Science, Technology, Engineering, and Mathematics (Chang & Chen, 2022 ; Luo et al., 2019 ). However, Science and Mathematics were given less weight in STEM learning activities compared to Technology and Engineering. This issue has been frequently reported as a barrier to implementing R-STEM in the interdisciplinary subject. Some reasons include difficulties in pedagogy and classroom roles, lack of curriculum integration, and a limited opportunity to embody one learning subject into others (Margot & Kettler, 2019 ). Therefore, further research is encouraged to treat these disciplines equally, so is the way of STEM learning integration.

The subject-matter results revealed that “programming” was the most common research focus in R-STEM research (25 studies). Researchers considered programming because this particular topic was frequently emphasized in their studies (Chang & Chen, 2020 , 2022 ; Newton et al., 2020 ). Similarly, programming concepts were taught through support robots for kindergarteners (Sullivan & Bers, 2019 ), girls attending summer camps (Chapman et al., 2020 ), and young learners with disabilities (Lamptey et al., 2021 ). Because programming simultaneously accompanies students’ STEM learning, we believe future research can incorporate a more dynamic and comprehensive learning focus. Robotics-based STEM education research is expected to encounter many interdisciplinary learning issues.

Researchers in the reviewed studies agreed that the robot could be integrated with STEM learning with various integration forms. Bryan et. al. ( 2015 ) argued that robots were designed to develop multiple learning goals from STEM knowledge, beginning with an initial learning context. It is parallel with our finding that robot-STEM content integration was the most common integration form (22 studies, 56.41%). In this form, studies mainly defined their primary learning goals with one or more anchor STEM disciplines (e.g., Castro et al., 2018 ; Chang & Chen, 2020 ; Luo et al., 2019 ). The learning goals provided coherence between instructional activities and assessments that explicitly focused on the connection among STEM disciplines. As a result, students can develop a deep and transferable understanding of interdisciplinary phenomena and problems through emphasizing the content across disciplines (Bryan et al., 2015 ). However, the findings on learning instruction and evaluation in this integration are inconclusive. A better understanding of the embodiment of learning contexts is needed, for instance, whether instructions are inclusive, socially relevant, and authentic in the situated context. Thus, future research is needed to identify the quality of instruction and evaluation and the specific characteristics of robot-STEM integration. This may place better provision of opportunities for understanding the form of pedagogical content knowledge to enhance practitioners’ self-efficacy and pedagogical beliefs (Chen et al., 2021a , 2021b ).

Project-based learning (PBL) was the most used instructional intervention with robots in R-STEM education (15 studies, 38.46%). Blumenfeld et al. ( 1991 ) credited PBL with the main purpose of engaging students in investigating learning models. In the case of robotics, students can create robotic artifacts (Spolaôr & Benitti, 2017 ). McDonald and Howell ( 2012 ) used robotics to develop technological skills in lower grades. Leonard et. al. ( 2016 ) used robots to engage and develop students’ computational thinking strategies in another example. In the aforementioned study, robots were used to support learning content in informal education, and both teachers and students designed robotics experiences aligned with the curriculum (Bernstein et al., 2022 ). As previously mentioned, this study is an example of how robots can cover STEM content from the learning domain to support educational goals.

The educational goal of R-STEM education was the last finding of our study. Most of the reviewed studies focused on learning and transferable skills as their goals (23 studies, 58.97%). They targeted learning because the authors investigated the effectiveness of R-STEM learning activities (Castro et al., 2018 ; Convertini, 2021 ; Konijn & Hoorn, 2020 ; Ma et al., 2020 ) and conceptual knowledge of STEM disciplines (Barak & Assal, 2018 ; Gomoll et al., 2017 ; Jaipal-Jamani & Angeli 2017 ). They targeted transferable skills because they require learners to develop individual competencies in STEM skills (Kim et al., 2018 ; McDonald & Howell, 2012 ; Sullivan & Bers, 2016 ) and to master STEM in actual competition-related skills (Chiang et al., 2020 ; Hennessy Elliott, 2020 ).

Conclusions and implications

The majority of the articles examined in this study referred to theoretical frameworks or certain applications of pedagogical theories. This finding contradicts Atman Uslu et. al. ( 2022 ), who concluded that most of the studies in this domain did not refer to pedagogical approaches. Although we claim the employment pedagogical frameworks in the examined articles exist, those articles primarily did not consider a strict instructional design when employing robots in STEM learning. Consequently, the discussions in the studies did not include how the learning–teaching process affords students’ positive perceptions. Therefore, both practitioners and researchers should consider designing learning instruction using robots in STEM education. To put an example, the practitioners may regard students’ zone of proximal development (ZPD) when employing robot in STEM tasks. Giving an appropriate scaffolding and learning contents are necessary for them to enhance their operational skills, application knowledge and emotional development. Although the integration between robots and STEM education was founded in the reviewed studies, it is worth further investigating the disciplines in which STEM activities have been conducted. This current review found that technology and engineering were the subject areas of most concern to researchers, while science and mathematics did not attract as much attention. This situation can be interpreted as an inadequate evaluation of R-STEM education. In other words, although those studies aimed at the interdisciplinary subject, most assessments and evaluations were monodisciplinary and targeted only knowledge. Therefore, it is necessary to carry out further studies in these insufficient subject areas to measure and answer the potential of robots in every STEM field and its integration. Moreover, the broadly consistent reporting of robotics generally supporting STEM content could impact practitioners only to employ robots in the mainstream STEM educational environment. Until that point, very few studies had investigated the prominence use of robots in various and large-scale multidiscipline studies (e.g., Christensen et al., 2015 ).

Another finding of the reviewed studies was the characteristic of robot-STEM integration. Researchers and practitioners must first answer why and how integrated R-STEM could be embodied in the teaching–learning process. For example, when robots are used as a learning tool to achieve STEM learning objectives, practitioners are suggested to have application knowledge. At the same time, researchers are advised to understand the pedagogical theories so that R-STEM integration can be flexibly merged into learning content. This means that the learning design should offer students’ existing knowledge of the immersive experience in dealing with robots and STEM activities that assist them in being aware of their ideas, then building their knowledge. In such a learning experience, students will understand the concept of STEM more deeply by engaging with robots. Moreover, demonstration of R-STEM learning is not only about the coherent understanding of the content knowledge. Practitioners need to apply both flexible subject-matter knowledge (e.g., central facts, concepts and procedures in the core concept of knowledge), and pedagogical content knowledge, which specific knowledge of approaches that are suitable for organizing and delivering topic-specific content, to the discipline of R-STEM education. Consequently, practitioners are required to understand the nature of robots and STEM through the content and practices, for example, taking the lead in implementing innovation through subject area instruction, developing collaboration that enriches R-STEM learning experiences for students, and being reflective practitioners by using students’ learning artifacts to inform and revise practices.

Limitations and recommendations for future research

Overall, future research could explore the great potential of using robots in education to build students’ knowledge and skills when pursuing learning objectives. It is believed that the findings from this study will provide insightful information for future research.

The articles reviewed in this study were limited to journals indexed in the WOS database and R-STEM education-related SSCI articles. However, other databases and indexes (e.g., SCOPUS, and SCI) could be considered. In addition, the number of studies analyzed was relatively small. Further research is recommended to extend the review duration to cover the publications in the coming years. The results of this review study have provided directions for the research area of STEM education and robotics. Specifically, robotics combined with STEM education activities should aim to foster the development of creativity. Future research may aim to develop skills in specific areas such as robotics STEM education combined with the humanities, but also skills in other humanities disciplines across learning activities, social/interactive skills, and general guidelines for learners at different educational levels. Educators can design career readiness activities to help learners build self-directed learning plans.

Availability of data and materials

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

Science, technology, engineering, and mathematics

Robotics-based STEM

Project-based learning

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Acknowledgements

The authors would like to express their gratefulness to the three anonymous reviewers for providing their precious comments to refine this manuscript.

This study was supported by the Ministry of Science and Technology of Taiwan under contract numbers MOST-109-2511-H-011-002-MY3 and MOST-108-2511-H-011-005-MY3; National Science and Technology Council (TW) (NSTC 111-2410-H-031-092-MY2); Soochow University (TW) (111160605-0014). Any opinions, findings, conclusions, and/or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of Ministry of Science and Technology of Taiwan.

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DD, MR and GJ conceptualized the study. MR wrote the outline and DD wrote draft. DD, MR and GJ contributed to the manuscript through critical reviews. DD, MR and GJH revised the manuscript. DD, MR and GJ finalized the manuscript. DD edited the manuscript. MR and GJ monitored the project and provided adequate supervision. DD, MR and JC contributed with data collection, coding, analyses and interpretation. All authors read and approved the final manuscript.

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Appendix 1. Summary of selected studies from the angle of research issue

Appendix 2. Summary of selected studies from the angles of interaction and application

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Darmawansah, D., Hwang, GJ., Chen, MR.A. et al. Trends and research foci of robotics-based STEM education: a systematic review from diverse angles based on the technology-based learning model. IJ STEM Ed 10 , 12 (2023). https://doi.org/10.1186/s40594-023-00400-3

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The recent history of robotics is full of fascinating moments that accelerated the rapid technological advances in artificial intelligence , automation , engineering, energy storage, and machine learning. The result transformed the capabilities of robots and their ability to take over tasks once carried out by humans at factories, hospitals, farms, etc.

These technological advances don’t occur overnight; they require several years of research and development in solving some of the biggest engineering challenges in navigation, autonomy, AI and machine learning to build robots that are much safer and efficient in a real-world situation. A lot of universities, institutes, and companies across the world are working tirelessly in various research areas to make this reality.

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Navigation and Motion Planning

• Robotics Navigation Using MPEG CDVS
• Design, Manufacturing and Test of a High-Precision MEMS Inclination Sensor for Navigation Systems in Robot-assisted Surgery
• Motion Control of a Three Active Wheeled Mobile Robot and Collision-Free Human Following Navigation in Outdoor Environment
• One Point Perspective Vanishing Point Estimation for Mobile Robot Vision Based Navigation System
• Application of Ant Colony Optimization for finding the Navigational path of Mobile Robot-A Review
• Robot Navigation Using a Brain-Computer Interface
• Path Generation for Robot Navigation using a Single Ceiling Mounted Camera
• Exact Robot Navigation Using Power Diagrams
• Learning Socially Normative Robot Navigation Behaviors with Bayesian Inverse Reinforcement Learning
• Pipelined, High Speed, Low Power Neural Network Controller for Autonomous Mobile Robot Navigation Using FPGA
• Proxemics models for human-aware navigation in robotics: Grounding interaction and personal space models in experimental data from psychology
• Optimality and limit behavior of the ML estimator for Multi-Robot Localization via GPS and Relative Measurements
• Aerial Robotics: Compact groups of cooperating micro aerial vehicles in clustered GPS denied environment
• Disordered and Multiple Destinations Path Planning Methods for Mobile Robot in Dynamic Environment
• Integrating Modeling and Knowledge Representation for Combined Task, Resource and Path Planning in Robotics
• Path Planning With Kinematic Constraints For Robot Groups
• Robot motion planning for pouring liquids
• Implan: Scalable Incremental Motion Planning for Multi-Robot Systems
• Equilibrium Motion Planning of Humanoid Climbing Robot under Constraints
• POMDP-lite for Robust Robot Planning under Uncertainty
• The RoboCup Logistics League as a Benchmark for Planning in Robotics
• Planning-aware communication for decentralised multi- robot coordination
• Combined Force and Position Controller Based on Inverse Dynamics: Application to Cooperative Robotics
• A Four Degree of Freedom Robot for Positioning Ultrasound Imaging Catheters
• The Role of Robotics in Ovarian Transposition
• An Implementation on 3D Positioning Aquatic Robot

Robotic Interactions

• On Indexicality, Direction of Arrival of Sound Sources and Human-Robot Interaction
• OpenWoZ: A Runtime-Configurable Wizard-of-Oz Framework for Human-Robot Interaction
• Privacy in Human-Robot Interaction: Survey and Future Work
• An Analysis Of Teacher-Student Interaction Patterns In A Robotics Course For Kindergarten Children: A Pilot Study
• Human Robotics Interaction (HRI) based Analysis–using DMT
• A Cautionary Note on Personality (Extroversion) Assessments in Child-Robot Interaction Studies
• Interaction as a bridge between cognition and robotics
• State Representation Learning in Robotics: Using Prior Knowledge about Physical Interaction
• Eliciting Conversation in Robot Vehicle Interactions
• A Comparison of Avatar, Video, and Robot-Mediated Interaction on Users’ Trust in Expertise
• Exercising with Baxter: Design and Evaluation of Assistive Social-Physical Human- Robot Interaction
• Using Narrative to Enable Longitudinal Human- Robot Interactions
• Computational Analysis of Affect, Personality, and Engagement in HumanRobot Interactions
• Human-robot interactions: A psychological perspective
• Gait of Quadruped Robot and Interaction Based on Gesture Recognition
• Graphically representing child- robot interaction proxemics
• Interactive Demo of the SOPHIA Project: Combining Soft Robotics and Brain-Machine Interfaces for Stroke Rehabilitation
• Interactive Robotics Workshop
• Activating Robotics Manipulator using Eye Movements
• Wireless Controlled Robot Movement System Desgined using Microcontroller
• Gesture Controlled Robot using LabVIEW
• RoGuE: Robot Gesture Engine

Obstacle Avoidance

• Low Cost Obstacle Avoidance Robot with Logic Gates and Gate Delay Calculations
• Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance
• Controlling Obstacle Avoiding And Live Streaming Robot Using Chronos Watch
• Movement Of The Space Robot Manipulator In Environment With Obstacles
• Assis-Cicerone Robot With Visual Obstacle Avoidance Using a Stack of Odometric Data.
• Obstacle detection and avoidance methods for autonomous mobile robot
• Moving Domestic Robotics Control Method Based on Creating and Sharing Maps with Shortest Path Findings and Obstacle Avoidance
• Control of the Differentially-driven Mobile Robot in the Environment with a Non-Convex Star-Shape Obstacle: Simulation and Experiments
• A survey of typical machine learning based motion planning algorithms for robotics
• Linear Algebra for Computer Vision, Robotics , and Machine Learning
• Applying Radical Constructivism to Machine Learning: A Pilot Study in Assistive Robotics
• Machine Learning for Robotics and Computer Vision: Sampling methods and Variational Inference
• Rule-Based Supervisor and Checker of Deep Learning Perception Modules in Cognitive Robotics
• The Limits and Potentials of Deep Learning for Robotics
• Autonomous Robotics and Deep Learning
• A Unified Knowledge Representation System for Robot Learning and Dialogue

Computer Vision

• Computer Vision Based Chess Playing Capabilities for the Baxter Humanoid Robot
• Non-Euclidean manifolds in robotics and computer vision: why should we care?
• Topology of singular surfaces, applications to visualization and robotics
• On the Impact of Learning Hierarchical Representations for Visual Recognition in Robotics
• Focused Online Visual-Motor Coordination for a Dual-Arm Robot Manipulator
• Towards Practical Visual Servoing in Robotics
• Visual Pattern Recognition In Robotics
• Automated Visual Inspection: Position Identification of Object for Industrial Robot Application based on Color and Shape
• Automated Creation of Augmented Reality Visualizations for Autonomous Robot Systems
• Implementation of Efficient Night Vision Robot on Arduino and FPGA Board
• On the Relationship between Robotics and Artificial Intelligence
• Artificial Spatial Cognition for Robotics and Mobile Systems: Brief Survey and Current Open Challenges
• Artificial Intelligence, Robotics and Its Impact on Society
• The Effects of Artificial Intelligence and Robotics on Business and Employment: Evidence from a survey on Japanese firms
• Artificially Intelligent Maze Solver Robot
• Artificial intelligence, Cognitive Robotics and Human Psychology
• Minecraft as an Experimental World for AI in Robotics
• Impact of Robotics, RPA and AI on the insurance industry: challenges and opportunities

Probabilistic Programming

• On the use of probabilistic relational affordance models for sequential manipulation tasks inrobotics
• Exploration strategies in developmental robotics: a unified probabilistic framework
• Probabilistic Programming for Robotics
• New design of a soft-robotics wearable elbow exoskeleton based on Shape Memory Alloy wires actuators
• Design of a Modular Series Elastic Upgrade to a Robotics Actuator
• Applications of Compliant Actuators to Wearing Robotics for Lower Extremity
• Review of Development Stages in the Conceptual Design of an Electro-Hydrostatic Actuator for Robotics
• Fluid electrodes for submersible robotics based on dielectric elastomer actuators
• Cascaded Control Of Compliant Actuators In Friendly Robotics

Collaborative Robotics

• Interpretable Models for Fast Activity Recognition and Anomaly Explanation During Collaborative Robotics Tasks
• Collaborative Work Management Using SWARM Robotics
• Collaborative Robotics : Assessment of Safety Functions and Feedback from Workers, Users and Integrators in Quebec
• Accessibility, Making and Tactile Robotics : Facilitating Collaborative Learning and Computational Thinking for Learners with Visual Impairments
• Trajectory Adaptation of Robot Arms for Head-pose Dependent Assistive Tasks

Mobile Robotics

• Experimental research of proximity sensors for application in mobile robotics in greenhouse environment.
• Multispectral Texture Mapping for Telepresence and Autonomous Mobile Robotics
• A Smart Mobile Robot to Detect Abnormalities in Hazardous Zones
• Simulation of nonlinear filter based localization for indoor mobile robot
• Integrating control science in a practical mobile robotics course
• Experimental Study of the Performance of the Kinect Range Camera for Mobile Robotics
• Planification of an Optimal Path for a Mobile Robot Using Neural Networks
• Security of Networking Control System in Mobile Robotics (NCSMR)
• Vector Maps in Mobile Robotics
• An Embedded System for a Bluetooth Controlled Mobile Robot Based on the ATmega8535 Microcontroller
• Experiments of NDT-Based Localization for a Mobile Robot Moving Near Buildings
• Hardware and Software Co-design for the EKF Applied to the Mobile Robotics Localization Problem
• Design of a SESLogo Program for Mobile Robot Control
• An Improved Ekf-Slam Algorithm For Mobile Robot
• Intelligent Vehicles at the Mobile Robotics Laboratory, University of Sao Paolo, Brazil [ITS Research Lab]
• Introduction to Mobile Robotics
• Miniature Piezoelectric Mobile Robot driven by Standing Wave
• Mobile Robot Floor Classification using Motor Current and Accelerometer Measurements
• Sensors for Robotics 2015
• An Automated Sensing System for Steel Bridge Inspection Using GMR Sensor Array and Magnetic Wheels of Climbing Robot
• Sensors for Next-Generation Robotics
• Multi-Robot Sensor Relocation To Enhance Connectivity In A WSN
• Automated Irrigation System Using Robotics and Sensors
• Design Of Control System For Articulated Robot Using Leap Motion Sensor
• Automated configuration of vision sensor systems for industrial robotics

Nano robotics

• Light Robotics: an all-optical nano-and micro-toolbox
• Light-driven Nano- robotics
• Light-driven Nano-robotics
• Light Robotics: a new tech–nology and its applications
• Light Robotics: Aiming towards all-optical nano-robotics
• NanoBiophotonics Appli–cations of Light Robotics
• System Level Analysis for a Locomotive Inspection Robot with Integrated Microsystems
• High-Dimensional Robotics at the Nanoscale Kino-Geometric Modeling of Proteins and Molecular Mechanisms
• A Study Of Insect Brain Using Robotics And Neural Networks

Social Robotics

• Integrative Social Robotics Hands-On
• ProCRob Architecture for Personalized Social Robotics
• Definitions and Metrics for Social Robotics, along with some Experience Gained in this Domain
• Transmedia Choreography: Integrating Multimodal Video Annotation in the Creative Process of a Social Robotics Performance Piece
• Co-designing with children: An approach to social robot design
• Toward Social Cognition in Robotics: Extracting and Internalizing Meaning from Perception
• Human Centered Robotics : Designing Valuable Experiences for Social Robots
• Preliminary system and hardware design for Quori, a low-cost, modular, socially interactive robot
• Socially assistive robotics: Human augmentation versus automation
• Tega: A Social Robot

Humanoid robot

• Compliance Control and Human-Robot Interaction – International Journal of Humanoid Robotics
• The Design of Humanoid Robot Using C# Interface on Bluetooth Communication
• An Integrated System to approach the Programming of Humanoid Robotics
• Humanoid Robot Slope Gait Planning Based on Zero Moment Point Principle
• Literature Review Real-Time Vision-Based Learning for Human-Robot Interaction in Social Humanoid Robotics
• The Roasted Tomato Challenge for a Humanoid Robot
• Remotely teleoperating a humanoid robot to perform fine motor tasks with virtual reality

Cloud Robotics

• CR3A: Cloud Robotics Algorithms Allocation Analysis
• Cloud Computing and Robotics for Disaster Management
• ABHIKAHA: Aerial Collision Avoidance in Quadcopter using Cloud Robotics
• The Evolution Of Cloud Robotics: A Survey
• Sliding Autonomy in Cloud Robotics Services for Smart City Applications
• CORE: A Cloud-based Object Recognition Engine for Robotics
• A Software Product Line Approach for Configuring Cloud Robotics Applications
• Cloud robotics and automation: A survey of related work
• ROCHAS: Robotics and Cloud-assisted Healthcare System for Empty Nester

Swarm Robotics

• Evolution of Task Partitioning in Swarm Robotics
• GESwarm: Grammatical Evolution for the Automatic Synthesis of Collective Behaviors in Swarm Robotics
• A Concise Chronological Reassess Of Different Swarm Intelligence Methods With Multi Robotics Approach
• The Swarm/Potential Model: Modeling Robotics Swarms with Measure-valued Recursions Associated to Random Finite Sets
• The TAM: ABSTRACTing complex tasks in swarm robotics research
• Task Allocation in Foraging Robot Swarms: The Role of Information Sharing
• Robotics on the Battlefield Part II
• Implementation Of Load Sharing Using Swarm Robotics
• An Investigation of Environmental Influence on the Benefits of Adaptation Mechanisms in Evolutionary Swarm Robotics

Soft Robotics

• Soft Robotics: The Next Generation of Intelligent Machines
• Soft Robotics: Transferring Theory to Application,” Soft Components for Soft Robots”
• Advances in Soft Computing, Intelligent Robotics and Control
• The BRICS Component Model: A Model-Based Development Paradigm For ComplexRobotics Software Systems
• Soft Mechatronics for Human-Friendly Robotics
• Seminar Soft-Robotics
• Special Issue on Open Source Software-Supported Robotics Research.
• Soft Brain-Machine Interfaces for Assistive Robotics: A Novel Control Approach
• Towards A Robot Hardware ABSTRACT ion Layer (R-HAL) Leveraging the XBot Software Framework

Service Robotics

• Fundamental Theories and Practice in Service Robotics
• Natural Language Processing in Domestic Service Robotics
• Localization and Mapping for Service Robotics Applications
• Designing of Service Robot for Home Automation-Implementation
• Benchmarking Speech Understanding in Service Robotics
• The Cognitive Service Robotics Apartment
• Planning with Task-oriented Knowledge Acquisition for A Service Robot
• Cognitive Robotics
• Meta-Morphogenesis theory as background to Cognitive Robotics and Developmental Cognitive Science
• Experience-based Learning for Bayesian Cognitive Robotics
• Weakly supervised strategies for natural object recognition in robotics
• Robotics-Derived Requirements for the Internet of Things in the 5G Context
• A Comparison of Modern Synthetic Character Design and Cognitive Robotics Architecture with the Human Nervous System
• PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains
• The Role of Intention in Cognitive Robotics
• On Cognitive Learning Methodologies for Cognitive Robotics
• Relational Enhancement: A Framework for Evaluating and Designing Human-RobotRelationships
• A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering
• Spatial Cognition in Robotics
• IOT Based Gesture Movement Recognize Robot
• Deliberative Systems for Autonomous Robotics: A Brief Comparison Between Action-oriented and Timelines-based Approaches
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Neural admittance control based on motion intention estimation and force feedforward compensation for human–robot collaboration

• Regular Paper
• Published: 22 July 2024

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• Wenxu Ai 1 , 2 , 3 ,
• Xinan Pan 1 , 2 ,
• Yong Jiang 4 &
• Hongguang Wang 1 , 2  

To enhance robotic manipulator adaptation to human partners and minimize human energy consumption in human–robot collaboration, this paper introduces a neural admittance control framework, which integrates human motion intention estimation and force feedforward compensation. Maximum likelihood estimation is employed to derive human motion intention and stiffness within human–robot collaboration, which are seamlessly merged into admittance control. Force feedforward compensation is proposed to minimize interaction force and position tracking fluctuations based on estimated human intention and stiffness. RBF neural network control is used to refine position inner loop dynamics and to improve position tracking accuracy and response speed. Comprehensive comparative simulations validate the method’s effectiveness in diminishing human–robot interaction force, enhancing position response speed, and mitigating interaction force and position fluctuations. The experiment performed on the Franka Emika Panda robot platform, illustrates that the proposed method is effective and enhance human-robot collaboration.

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Adaptive Human-Robot Collaboration Control Based on Optimal Admittance Parameters

Series admittance–impedance controller for more robust and stable extension of force control

A neural network-based approach for variable admittance control in human–robot cooperation: online adjustment of the virtual inertia

Data availability.

Experimental data can be obtained from the corresponding author upon reasonable request.

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Funding This study was supported in part by the National Natural Science Foundation of China under Grant U20A20282 and Grant 52075531, in part by the International Science and Technology Cooperation Program of Liaoning Province under Grant 2023JH2/10700008, in part by the Applied Fundamental Research Program of Liaoning Province under Grant 2022JH2/101300205, and in part by the Fundamental Research Program of Shenyang Institute of Automation under Grant 2022JC3K06, and in part by the Fundation of State Key Laboratory of Robotics under Grant No. 2017-Z02.

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State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

Wenxu Ai, Xinan Pan & Hongguang Wang

Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China

University of Chinese Academy of Sciences, Beijing, 100049, China

Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang, 212013, China

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WA contributed to the study conception and design, experiments and writing were also performed by WA. XP wrote, modified the initial draft and provided funding. YJ provided funding and ideas. HW provided funding and editing. All authors reviewed the manuscript.

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Ai, W., Pan, X., Jiang, Y. et al. Neural admittance control based on motion intention estimation and force feedforward compensation for human–robot collaboration. Int J Intell Robot Appl (2024). https://doi.org/10.1007/s41315-024-00362-x

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Published : 22 July 2024

DOI : https://doi.org/10.1007/s41315-024-00362-x

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Research review and future directions of key technologies for welding robots in the construction industry.

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Bu, H.; Cui, X.; Huang, B.; Peng, S.; Wan, J. Research Review and Future Directions of Key Technologies for Welding Robots in the Construction Industry. Buildings 2024 , 14 , 2261. https://doi.org/10.3390/buildings14082261

Bu H, Cui X, Huang B, Peng S, Wan J. Research Review and Future Directions of Key Technologies for Welding Robots in the Construction Industry. Buildings . 2024; 14(8):2261. https://doi.org/10.3390/buildings14082261

Bu, Han, Xiaolu Cui, Bo Huang, Shuangqian Peng, and Jiuyu Wan. 2024. "Research Review and Future Directions of Key Technologies for Welding Robots in the Construction Industry" Buildings 14, no. 8: 2261. https://doi.org/10.3390/buildings14082261

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Simulation research on Tai Chi movement posture resolution based on multi-MEMS sensor combination

• Benzheng, Wang

The combined system based on multiple MEMS sensors is a miniature measurement system used for dynamic output and display of 3D information about the user's posture. It is mainly used for various Tai Chi movement posture calculation simulation research, wearable devices, etc. This article explores MEMS sensor technology, focusing on MEMS sensor data processing, Tai Chi movement position calculation and fusion calculation positioning algorithm. Due to the high noise characteristics of MEMS sensor devices, time series analysis is used to model MIMU signals and Kalman filtering is optimized. As a research field, simulation of Tai Chi movement appears in the intersection of biomechanics, robotics and computer science. The purpose is to create a computer model to simulate the natural and real body movements of the human body under certain conditions. In addition to creating special effects, Tai Chi movement posture calculation simulation can also be used for operation training and research on body structure. This article first introduces the typical applications of several MEMS sensor combinations, and then introduces the key technology of studying Tai Chi movement simulation. The kinematics and mechanics data of Tai Chi are obtained using biomechanical measurement technology, while the individual simulation of Tai Chi dynamics is realized in a certain mode of the machine. By creating a kinematic model of the human upper limb, and finally creating a flexible machine that imitates the human upper limb, to analyze the kinematic characteristics of the human upper limb, and cleverly realize the imitation of active interaction, the simulation of human movement and the solution of Tai Chi movement posture Simulation.

• Combination of multiple MEMS sensors;
• Tai Chi movement;
• Attitude calculation;
• Simulation research

• Biomedical & Life Sciences
• Business & Economics
• Chemistry & Materials Science
• Computer Science & Communications
• Engineering
• Medicine & Healthcare
• Physics & Mathematics
• Social Sciences & Humanities
• LinkedIn (OALib Group)
• LinkedIn (OALib Company Page)

ISSN: 2333-9721 APC: Only $99 Views Downloads Relative Articles Utilisation of Robotic Systems in Automating Manufacturing Applications While Reducing the Amount of Labour and Production Costs and Time Associated With the Process DOI: 10.36647/TTIRAS/02.01.A004 , PP. 20-26 1 IRCCS, Don Gnocchi Foundation, Milan, Italy 2 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy 3 Università degli Studi di Milano, Milan, Italy Keywords: Articulated robots , automation process , cost , cyber security , Delta robot , labour , manufacturing industry , robotic process automation (RPA) , robotic system , time       -->    This article is based on the robotic system in automating manufacturing applications and industry. Robotics is the assembles of various kinds of technology, devices, and others kinds of mechanisms. Cameras, sensors, artificial voice, microphones, and motors are the fundamental requirement that needs scientists to build a robot. Automating manufacturing applications helps to boost productivity and also creates a modern industrial aspect. Robotics provides opportunities to build an updated working place. It can able to reduce production costs and also provides technical knowledge to the laborers. Additionally automating the manufacturing process can detect system-related issues and suggest ways for reducing the issues. Robotics is one of the innovations that help to reduce the pressure of labour. This study has shed light on the purpose of robotic systems in automation manufacturing and known their impact on the manufacturing process. Accordingly, this article also talked about the types of robotic systems that are necessary for the manufacturing industry. In addition, this paper tries to analyze the collected data and also shines a light on the future application of robotics and its outcomes in the manufacturing industry. This paper has preferred the secondary qualitative process for the doing entire research work and it can be said that this is one of the smooth ways to get outstanding outcomes from this particular subject matter. Apart from this, robotics in automating applications is one of the curious subjects that help to gain more intellectual information to increase experiences for the future. [email protected] QQ: 3279437679 微信:OALib Journal WhatsApp +8615387084133 WeChat 1538708413 An official website of the United States government The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site. The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. Publications Account settings Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now . Advanced Search Journal List HHS Author Manuscripts Nanorobotic Applications in Medicine: Current Proposals and Designs Advances in technology have increased our ability to manipulate the world around us on an ever-decreasing scale. Nanotechnologies are rapidly emerging within the realm of medicine, and this subfield has been termed nanomedicine. Use of nanoparticle technology has become familiar and increasingly commonplace, especially with pharmaceutical technology. An exciting and promising area of nanotechnological development is the building of nanorobots, which are devices with components manufactured on the nanoscale. This area of study is replete with potential applications, many of which are currently being researched and developed. The goal of this paper is to give an introduction to the emerging field of nanorobotics within medicine, and provide a review of the emerging applications of nanorobotics to fields ranging from neurosurgery to dentistry. Introduction Progression in science and medicine has been marked by the ability of researchers to study and understand the world around us on a progressively smaller scale. With each order of magnitude of access to smaller dimensions, new therapeutic possibilities and frameworks of understandings were developed. These developments included the germ theory and microbiology The next phase in the ever-decreasing size of operation is the development of nanotechnology, where researchers are able to work on the scale of nanometers. The scale of nanotechnology is defined by the National Nanotechnology Initiative (NNI), a United State government initiative to promote the development of nanotechnology research and development, as “science, research, and technology conducted on the nanoscale.” The NNI defines this scale as approximately 1 to 100 nanometers. To give a practical idea of the nanoscale, a cell surface receptor is approximately 40 nanometers 1 , a strand of DNA is about 2 nanometers in diameter, and a molecule of albumin is about 7 nanometers. To date, some examples of what nanotechnology has enabled include the development of improved imaging techniques for higher sensitivity in detection of cancer and illness 2 , improved targeting of drug treatments 3 , decrease in the number of adverse effects of chemotherapy, and the enhanced effectiveness of other antineoplastic therapies such as cryotherapy 4 and ultrasound 5 . Outside of medicine, nanotechnology is also fueling developoments in agriculture 6 , energy 7 , electronics 8 , and many other fields. The concept of nanotechnology is reported to have first been envisioned by the celebrated physicist Dr. Richard Feynman, during a lecture called “There's Plenty of Room at the Bottom,” which was delivered to the American Physical Society in December of 1959. Dr. Feynman discussed the field and scale of nanotechnology in principle, and the possibilities it would unlock for biological research, information technology, manufacturing, electrical engineering, and other fields 9 . Nanobiotechnology is a subfield of nanotechnology that uses the principles and techniques of nanotechnology and applies them towards research and advancement in the biological sciences and medicine. Nanobiotechnology involves the development of technology such as pharmaceuticals and mechanical devices at the nanometer scale for the study of biological systems and treatment of pathology 10 . This article will focus on the advances of nanobiotechnology in the realm of device development, specifically on the construction of nanorobotics and their application in the medical field. Representative examples from the fields of microbiology, hematology, oncology, neurosurgery, and dentistry will be reviewed. Microbiology The field of microbiology has been successfully used as a springboard for the initial development of robotic functions in nanobiotechnology. Although microrobots and nanorobots can be constructed and have function 11 , their use within the vascular system is limited by challenges with transportation and propulsion. An effective strategy for enabling propulsion of microrobots and nanorobots is coupling them to magnetotactic bacteria such as Magnetococcus, Magnetospirillum magnetotacticum or Magnetospirillum magneticum 12 , 13 . The largest componenet of these nanorobots integrated into magnetotactic bacteria would be the bacterial cell component. The smallest known species of magnetotactic bacteria is the marine magnetotactic spirillum, which is 0.5 μm (500 nanometers), just above the upper limit of the NNI's definition of the nanoscale 14 . However, the marine magnetotactic spirillum's usefulness is limited by their speed, and magnetotactic cocci are more useful for intravascular function 14 . The magnetotactic bacteria can be guided in the desired direction using the application of magnetic fields 15 . The components of the magnetotactic bacteria that are responsive to the magnetic field are called magnetosomes. Magnetosomes are prokaryotic pseudo-organelles with about 15-20 magnetite crystals, each about 50 nm in diameter, contained within an invagination of the prokaryotic cell membrane 16 . Magnetite crystals are composed of Fe 3 O 4 , a common iron oxide. Magnetotactic cocci have been found to travel in consistent and predictable patterns following established geomagnetic lines 17 . There are several theorized practical uses to the development of such a device. A highly customizable structure can be ligated to the bacteria, containing therapeutic compounds such as pharmaceuticals and artificial antibodies for function at the target site 18 . There is also the potential for use of these device to collect information and function as sensors 19 . Larger robots have higher ability to function in and navigate through larger vessels with limited function in capillaries and small vessels. Smaller nanorobots are highly useful in capillary environments and the microvasculature, but cannot achieve high enough velocities for control in large vessels. A two-component robotic system including a larger system for transport and control through large vessels, followed by release of the smaller component into small vessels has been proposed, and is a promising idea for pursuing practical development this field 20 . There is a rich base of research and potential applications for nanomedicine and nanorobotic applications in the field of hematology. From uses ranging to emergency transfusions of non-blood oxygen carrying compounds to restoring primary hemostasis, there is a wide array of applications under study for nanorobotics in hematology 21 . One of these devices currently under design is a nanorobot dubbed a respirocyte. This robot is equipped to have three functions as it travels through the bloodstream. First, collecting oxygen as it passes through the respiratory system for distribution throughout the bloodstream. Second, collecting carbon dioxide from tissues for release into the lungs. And finally, metabolizing circulating glucose to power its own functions 22 . The total size of the robot would be about one micron, or 1,000 nanometers. However, the contained components would be constructed on the nanoscale. These include an onboard computer of 58 nm diameter, and oxygen and carbon dioxide loading rotors with a maximum 14 nm diameter in any one dimension 22 . The respirocyte is designed to carry 236 times more oxygen per unit of volume compared to red blood cells 22 . Development and use of this technology could provide an effective and lower risk alternative to blood transfusions. The process of hemostasis is another area where nanorobotics may have applicability. Hemostasis is a sophisticated process involving several steps with a number of promoters and inhibitors balancing thrombosis and fibrinolysis 23 . When hemostasis works appropriately, it can be very effective in halting bleeding and promoting vessel repair. However, there are natural limitations to physiologic hemostasis, such as an average bleeding time of about five minutes 24 , that can be improved upon by nanorobotics. Additionally, when there is an impairment of our physiologic hemostatic mechanisms, such as with thrombocytopenia, our current methods of correcting this impairment have inherent risks. Patients undergoing platelet transfusions risk infection with pathogens and the potential of triggering an immune response 25 . The proposed nanorobot for this function has been termed an artificial mechanical platelet, or “clottocyte” 26 . The potential design parameters for this device have been described as a two micron nanorobot, containing a mesh as thin as 0.8 nm and inundated with hemostasis promoting proteins, which is fired at areas of vessel injury to carry out hemostasis 26 . Finally, another potential use of nanorobots in this arena is as phagocytic agents 27 . These nanorobots have been termed “Microbivores.” These robots would be designed to have a large number of customizable binding sites on their external surface, for antigens or pathogens for anything from HIV to E. Coli 28 . Microbivores are theorized to be as much as 80 times more effective than our physiologic phagocytic capabilities, and could have the potential to clear septicemia within hours of administration 29 . With the alarming rise in antibacterial resistance, developing nanorobotical capabilities to battle infection may open promising avenues for treatment of infection. A field in which nanorobots can have significant routine and specialized use is the field of dentistry. Virtually all the elements of dental care and treatment could incorporate nanorobots and benefit from their use by providing a higher level of care. These uses range from a routine cleaning, to cosmetics and teeth whitening, hypersensitivity, and even orthodontics 30 . Nanorobots can be incorporated into almost every aspect of dental care, including the initial analgesia a dentist may give at the start of a visit. A suspension containing millions of nanorobots is administered orally to the patient 31 . These robots are small enough to enter the gingival sulcus, and eventually travel through the micron sized dental tubules to reach the pulp 32 . Central control of these nanorobots would allow activation of analgesic activity in highly specific areas in proximity to where the dentist will be providing care 33 . Use of nanorobots in procedures such as root canal fillings or in the treatment of infection is also plausible. As discussed earlier, nanorobots can be enveloped in highly specific proteins to bind the targeted pathogens for the treatment of infection. For a procedure such as a root canal, the use of a tiny camera can provide visualization of the root, reducing any guesswork. Nanorobots can potentially increase the success rate of root canal procedures. In 2011, the National Health Service had a 70% success rate for root canal procedures, which leaves plenty of room for improvement 34 . Nanorobotics also has some potential function for the treatment of dental conditions such as dentine hypersensitivity. Studies have found that hypersensitive teeth can have significantly increased numbers of dentinal tubules compared to normal teeth, with the dentinal tubules also having a larger diameter than normal 35 . Penetration of nanorobots into these dentinal tubules, with selective ablation or occlusion of tubules within the hypersensitive teeth would prevent stimuli from penetrating and inducing a pain response 36 . Other potential applications of nanorobotics in dentistry range from tooth repositioning via direct manipulation of periodontal tissues, dental cosmetic work via the direct replacement of enamel layers, or even nanorobots incorporated into a mouthwash or toothpaste where they would enhance daily dental care 32 . Nanorobotics has a wide array of potential applications to dentistry, and holds much promise as an area of development. Neurosurgery Nanotechnology has progressed from a theoretical proposal to a rich area of proposals and ideas, and now is an active area of practical research and developments. As a field that frequently functions on a microscopic level, neurosurgery is uniquely suited to benefit from many of the innovations nanotechnology has to offer. These benefits include improved detection of pathology, minimally invasive intracranial monitoring, and pharmaceutical delivery, amongst many others 37 . The increase in our ability to work on an ever-decreasing scale has been greatly accelerated by advances in manufacturing microelectomechanical systems. These advances may allow manipulation on the scale of individual cells, and potentially on the molecular scale in the near future 38 . The topic of spinal cord injury and nerve damage is an important area of concern within neurosurgery as a field, and as a significant life-altering event for affected patients. The practice of reconnecting transected nerves has been done for more than 100 years, with progressive advancement in technique and technology. Currently, there are several different routes being pursued with the goal of optimizing and improving nerve reconnection outcomes, including promoting the regeneration of axons via growth factors 39 and enriched scaffolds 40 . Restoring connectivity to transected axons is an integral step to the restoration of function. The ability to do this is limited by technical limitations to surgery on that scale 41 . Advancements in technology have led to the development of devices on the nanoscale which allow manipulation of individual axons. A nanoknife with a 40 nanometer diameter has been developed and found to be effective for axon surgery 42 . The use of dielectrophoresis, which involves the use of electrical fields to manipulate polarizable objects in space, has been found to be effective in achieving controlled movement of axons within a surgical field 43 . Following controlled transection of axons and maneuvering them into position using dielectrophoresis, fusion between the two ends can be induced via electrofusion 44 , polyethylene glycol 45 , or laser-induced cell fusion 46 , amongst other methods. Nanodevices are enabling a new dimension of precision and control with the reconnection of nerves. One of the most effective ways to prevent morbidity and mortality in the field of neurosurgery is the treatment of cerebral aneurysms before rupture. Rupture of a cerebral aneurysm yields a high mortality rate. Ten percent of patients die before reaching the hospital, another 25% die within 24 hours of aneurysm rupture, and almost 50% die within 30 days 47 . There have been no cost-effective guidelines determined for screening patients for cerebral aneurysm. Nanorobotics can present a potential option for screening for a new aneurysm, or closer monitoring of an identified aneurysm. Cacalcanti et al have proposed a design for an intravascular nanorobot with the capability to detect aneurysm formation by detecting increased levels of nitric oxide synthase protein within the affected blood vessel 48 . These nanorobots can be given the capability to wirelessly communicate information about pertinent vascular changes to care providers, potentially decreasing screening costs of imaging and frequent follow up visits. Importantly, developing the platform required for this device will also enable horizontal expansion of the idea for many other uses, such as tumor detection or ischemic changes. Improving the treatment quality and clinical outcomes of cancer patients, and reducing the mortality and morbidity associated with oncological conditions and their treatment has been identified as a goal by the Institute of Medicine 49 . This need is underscored by the increasing number of seniors in the population, and the increasing number of cancer diagnoses that comes with an aging population. Nanotechnology has already shown much promise in improving the management of cancer. Increasing the sensitivity of cancer imaging tools 50 , overcoming drug resistance 51 , and improved treatment of metastasis 52 are some examples of nanoparticle technology's increasing role. There have also been some promising developments in the subfield of nanorobotics for the treatment of cancer, which will be discussed below. One of the limitations of conventional chemotherapy has been the toxic effects on normal cells by the chemotherapeutic agents limiting the dose. This limitation has been improved upon as targeted therapies have developed, and as nanoparticle technology has improved the selectivity of treatment 53 . The development of a nanorobot that can autonomously detect cancerous cells, and release treatment agents at the site of these cancerous cells has been successfully developed 54 . This nanorobot can be constructed to respond to a number of different cell surface receptors, and the payload it releases upon activation can also be changed as necessary. This nanorobot has been constructed using engineered DNA strands that have been made to fold into a desired tertiary structure 55 . Upon binding the desired target, the conformation of the DNA nanorobot undergoes a structural reconfiguration and shifts from a closed to an open state 54 , releasing the stored therapy. As the above example demonstrates, there is potential in the idea of an autonomous nanorobot circulating through the bloodstream with the ability to selectively release treatment only in the necessary areas. This can be accomplished through a nanorobot built of synthetic elements, in contrast to the biological elements of a DNA nanorobot. Freitas proposes the design of what he terms a pharmacyte, a nanorobot that also contains a therapeutic payload for the treatment of tumors. This nanorobot would have surface binding sites to bind selected targets, self-sufficient energy generation 56 , and locomotive function to move across tissue walls and cell membranes 57 . There have also been studies exploring the potential incorporation of nanorobots in tumor resection surgeries, to improve the detection and mapping of tumor margins intraoperatively. A similar approach not utilizing nanorobots has been explored and its efficacy demonstrated. The study demonstrated that using a radioactive colloid injection into the prostate the day prior to tumor resection, and then conducting radioisotope guided sentinel lymph node dissection was more sensitive in detecting early metastasis than open lymph node dissection 58 . The implementation of nanorobots can improve upon this procedure by eliminating the need for the patient to be admitted a day prior to the procedure and eliminating the risk of prostatitis associated with the injection. Nanorobots would be administered intravascularly during the procedure in order to detect tumorous tissue margins and metastatic areas. The nanorobots then conglomerate at sites where tumor tissue is present, and send an electromagnetic localizing signal to the operating surgeon for mapping 59 . Nanotechnology has created the opportunity for numerous ways to improve cancer therapy and as nanorobotic technology progresses, it is doubtless that more applications will be envisioned. Further development of the existing technology towards the proposed designs has the potential to establish new standards in the treatment, screening, and prevention of cancer. Though the premise of intravascular therapy for a diverse number of conditions has been described more than a century ago 60 , it has been over the past couple of decades that intravascular therapy has become established as a mainstay of treatment for conditions ranging from aneurysms and tumors to atherosclerosis. The development of nanotechnology has increased the efficacy of existing technologies and is leading the development of new methods for the treatment and prevention of disease through the vascular system 61 . We will give a brief overview of some of the emerging applications of nanorobotics towards intravascular therapies. The use of nanorobots intravascularly greatly expands the potential for screening and monitoring for life-threatening health conditions, as well as monitoring the development and progression of chronic diseases. Examples of life-threatening conditions that could be screened for include brain aneurysms 62 , cancers with no current screening protocols such as lung cancer 63 , and unstable atherosclerotic lesions 64 . Intravascular nanorobots would constantly circulate and provide current information at any desired moment. Integration with current technology would also allow constant syncing wirelessly, and immediate notifications of changes in health status. The monitoring of chronic health conditions such as diabetes 65 increases the capability for optimally managing chronic diseases. Improvements in primary prevention capabilities have been the hallmark of improved quality of life and life expectancy in our society, in addition to cost savings 66 . Intravascular nanorobots are potentially the next stage in the continued development of our primary prevention capabilities, and will likely contribute to making our health care system more lean and effective. In addition to screening and monitoring capabilities, nanorobots can be developed for the application of direct intravascular therapy. For example, in the case of coronary artery stenosis, nanorobots could provide direct therapy to the target area either mechanically or with pharmacologic treatment 67 . Nanorobots also have use in the prevention and acute treatment of aneurysm rupture. The intravascular navigational ability of a nanorobot can allow localized drug delivery to reduce the amount of bleeding, as well as a localization tool as an adjunct to imaging 68 . Additionally, nanorobots can be used for the detection and direct treatment of cancer 69 . The ability of intravascular nanorobots to constantly circulate can provide constant tumor surveillance. For treatment purposes, use of a nanorobot for direct local treatment delivery can improve efficacy by allowing delivery of a higher treatment dose due to a more limited volume of distribution resulting in lower potential toxicity. Intravascular nanorobotics is a promising area of current development within nanotechnology. The technological capabilities are present for these designs, and as the current proposals undergo development and proof of concept studies, it will be a number of years before nanorobotics enters the clinical environment on a widespread scale. As nanorobots begin to emerge as treatment adjuncts, they will improve efficacy of current treatments and our overall ability to prevent, detect, and treat illness. The scientific community is in the midst of a breakthrough in developing technology on a scale orders of magnitude smaller than ever before. As our technology advances, and as we explore on smaller and smaller scales, we are able to gain increased control of the world around us and ourselves. In the past, developing the ability to manipulate the world on a smaller scale brought transformative changes to the scientific community, and the world at large. Whether it was the age of microscopes ushering in the area of bacteriology, or the beginning of the atomic age with the study of particle physics, nanotechnology is poised to change many of the paradigms with which we think about disease diagnosis, treatment, prevention, and screening. Outside the bounds of medicine, nanotechnology will affect our lives in countless other ways through industries such as telecommunications and agriculture. This review provided a brief outline of nanodevices and nanorobotics in medicine, a small subset of the massive field of nanotechnology and nanobiotechnology (see table 1 for a summary of topics discussed). Nanorobotics are developing wide potential applications across all fields of medicine, and expanding the number of therapeutic options available, while also improving the efficacy of existing treatments. It is certainly possible within a generation of time that the use of nanorobotic technology will become ubiquitous in medicine. Overview of the existing and emerging nanorobotic applications across specialties of medicine. Specialty Brief Description Reference Microbiology Use of magnetotactic bacteria to transport and navigate nanorobots , , Hematology Circulating “respirocyte” nanorobots to deliver oxygen and return remove waste products from periphery Hematology Circulating “clottocyte “nanorobot with hemostatic functions Hematology Phagocytic “microbivores” with customizable antigen binding sites for targeting of pathogens Dentistry Dental anesthesia and sensitive teeth through nanorobot penetrating dentinal tubules for occlusion or administration of targeted analgesic , , , Dentistry Enhancement of the success rate of root canal procedures by providing visualization of root Dentistry Improved daily dental hygiene and teeth cosmetics by replacement of enamel layers Neurosurgery Single axon manipulation and transection with use nanoknife , Neurosurgery Circulating nanorobot for the monitoring of intracranial aneurysm development and progression Oncology Screening nanorobot circulating and monitoring for detection of neoplasia Oncology Direct drug delivery to cancerous tissue to limit systemic toxicity and increase effectiveness Oncology Mapping of margins of tumor to improve resection during surgery , Vascular Screening for atherosclerosis, cancer, aneurysms, and more - Vascular Localization of bleeding site for assisting embolization Disclosure of potential conflicts of interest: No conflict of Interest International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences E-ISSN: 2349-7300 • Impact Factor - 9.907 A widely indexed open access peer reviewed online scholarly international journal. Submit Your Research Authors can order CrossRef DOI for their research paper published in our journal IJIRMPS. DOI prefix of IJIRMPS is 10.37082/IJIRMPS Utilization of Automation and Robotics in Pharmacy Practice Authors: Hamdi Saleem Alharbi, Nasser Gaed Alsubaie, Faisal Mubarak Alharbi, Meshari Ali Aljedaee, Mohammed Rashed Aldhahri Country: Saudi Arabia Abstract: Automation and robotics have become an integral part of various industries, including healthcare. In pharmacy practice, these technologies play a crucial role in improving efficiency, accuracy, and safety of medication dispensing and management processes. This essay explores the implications of automation and robotics in pharmacy practice at the Master level, highlighting their benefits, challenges, and future prospects. Keywords: automation, robotics, pharmacy practice, medication management, efficiency, accuracy Paper Id: 230782 Published On: 2021-02-06 Published In: Volume 9, Issue 1, January-February 2021 Cite This: Utilization of Automation and Robotics in Pharmacy Practice - Hamdi Saleem Alharbi, Nasser Gaed Alsubaie, Faisal Mubarak Alharbi, Meshari Ali Aljedaee, Mohammed Rashed Aldhahri - IJIRMPS Volume 9, Issue 1, January-February 2021. ; accordingly, any user can read, download, copy, distribute, print, search, or link to the full texts of the authors/researchers submitted and published articles, crawl them for indexing, pass them as data to any software, or use them for any other lawful purpose. The journal is fulfilling the 's definition of open access. Essays and Short Answer Prompts The Penn application process includes a personal essay —which is sent to most schools you apply to—as well as a few short answer prompts . 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Additionally, please indicate why you are interested in pursuing dual degrees in science and engineering and which VIPER majors are most interesting to you at this time. (400-650 words)  362 IMAGES (PDF) Research Paper on Robotics-New Era (PDF) A review of the applicability of robots in education Top 10 Applications of Robotics in 2020 The Research and Application of Artificial Intelligence in the Field of Research Paper: Robotics and AI (PDF) Impact of Artificial Intelligence, Robotics, and Automation on VIDEO A framework for robotic excavation and dry stone construction using on-site materials Can a robot influence your decisions? It depends on how you view the machine 2024 May June Exam Paper 1 Question 2 Memo How Can I Effectively Read a Robotics Research Paper? FiSAED 2023: Opening Ceremony Plenary Session UR5 Robotic Reinforcement Learning (ROS/OpenAI Gym) (Untrained) COMMENTS Review of Robotics Technologies and Its Applications Abstract: Robots are automatic equipment integrating advanced technologies in multiple disciplines such as mechanics, electronics, control, sensors, and artificial intelligence. Based on a brief introduction of the development history of robotics, this paper reviews the classification of the type of robots, the key technologies involved, and the applications in various fields, analyze the ... (PDF) ARTIFICIAL INTELLIGENCE IN ROBOTICS: FROM ... This research paper explores the integration of artificial intelligence (AI) in robotics, specifically focusing on the transition from automation to autonomous systems. The paper provides an ... (PDF) Advanced Applications of Industrial Robotics: New ... three main functions in which robots replace hum ans: (1) extraction of useful information. from massive data flow; (2) accu rate movements to manipulate with an object or tool; and. (3 ... The International Journal of Robotics Research: Sage Journals International Journal of Robotics Research (IJRR) was the first scholarly publication on robotics research; it continues to supply scientists and students in robotics and related fields - artificial intelligence, applied mathematics, computer science, electrical and mechanical engineering - with timely, multidisciplinary material... This journal is peer-reviewed and is a member of the ... Language models are robotic planners: reframing plans as goal Successful application of large language models (LLMs) to robotic planning and execution may pave the way to automate numerous real-world tasks. Promising recent research has been conducted showing that the knowledge contained in LLMs can be utilized in making goal-driven decisions that are enactable in interactive, embodied environments. Nonetheless, there is a considerable drop in ... An overview of robot applications in automotive industry Abstract. The paper deals with an overview of industrial robot usage possibility in automotive industry which nowadays is the most important customer of industrial robotic market. The first part of paper describes the situation of industrial robot usage and offers an overview of application of robots in world industry. (PDF) SENSORS IN ROBOTICS AND ITS APPLICATIONS In this research, we assessed how well different types of sensors wo rked in various robotics applications. Four. types of sensors, namely proximity, ultrasonic, light, and temp erature sensors ... [2312.07843] Foundation Models in Robotics: Applications, Challenges In this survey, we study recent papers that have used or built foundation models to solve robotics problems. We explore how foundation models contribute to improving robot capabilities in the domains of perception, decision-making, and control. We discuss the challenges hindering the adoption of foundation models in robot autonomy and provide ... Swarm Robotics: Simulators, Platforms and Applications Review This paper presents an updated and broad review of swarm robotics research papers regarding software, hardware, simulators and applications. The evolution from its concept to its real-life implementation is presented. Swarm robotics analysis is focused on four aspects: conceptualization, simulators, real-life robotics for swarm use, and applications. For simulators and robots, a detailed ... The need for reproducible research in soft robotics Research comprises a dynamic interplay between discovery and distillation into practice. So far in soft robotics, novelty presides. Little emphasis has been placed on rigorous comparisons across ... Trends and research foci of robotics-based STEM ... The purpose of this study was to fill a gap in the current review of research on Robotics-based STEM (R-STEM) education by systematically reviewing existing research in this area. This systematic review examined the role of robotics and research trends in STEM education. A total of 39 articles published between 2012 and 2021 were analyzed. Swarm Robotics: Past, Present, and Future [Point of View] Swarm robotics deals with the design, construction, and deployment of large groups of robots that coordinate and cooperatively solve a problem or perform a task. It takes inspiration from natural self-organizing systems, such as social insects, fish schools, or bird flocks, characterized by emergent collective behavior based on simple local interaction rules [1], [2]. Typically, swarm robotics ... 500 research papers and projects in robotics These free, downloadable research papers can shed lights into the some of the complex areas in robotics such as navigation, motion planning, robotic interactions, obstacle avoidance, actuators, machine learning, computer vision, artificial intelligence, collaborative robotics, nano robotics, social robotics, cloud, swan robotics, sensors ... Development of a wheeled wall‐climbing robot with an internal corner The Journal of Field Robotics is an applied robotics journal publishing theoretical and practical papers on robotics used in real-world applications. Abstract The wall-climbing robot is a growing trend for robotized intelligent manufacturing of large and complex components in shipbuilding, petrochemical, and other industries, while several chall... Artificial Intelligence With Robotics in Healthcare: A Narrative Review Research papers related to the use of robotics and artificial intelligence in healthcare were thoroughly studied with special emphasis on its viability in the Indian scenario. The relevant search terms used were artificial intelligence, robotics, healthcare, India, etc. ... The application of robotics in surgery was first imagined in 1967, but ... Robotic Process Automation and Artificial Intelligence in Industry 4.0 In this context, this paper aims to present a study of the RPA tools associated with AI that can contribute to the improvement of the organizational processes associated with Industry 4.0. ... which uses robotics as a “set of techniques concerning the operation and use of automata (robots) in the execution of multiple tasks in place of ... Neural admittance control based on motion intention ... Academic research focuses on incorporating the proficiency of robots in high-precision, efficient, and repetitive tasks, and the innate strengths of humans in understanding, perception, and decision-making. Impedance control or admittance control Hogan is a key in human-robot collaboration. The robot adopts a passive role, following human ... Soft Robotics: A Systematic Review and Bibliometric Analysis The first identified bibliometric analysis conducted in the field of soft robotics was that of Bao et al. , who retrieved data from the WOS database for studies published between 1990 and May 2017 using a range of keywords relevant to the field, which resulted in 1495 review and research articles being selected; in that paper numerous different ... Mobile robotics in smart farming: current trends and applications 2. Research methodology. A systematic literature review (SLR) was performed to manage the diverse knowledge and identify research related to the raised topic (Ahmed et al., 2016), especially to investigate the status of mobile robotics in precision agriculture.In particular, we searched for papers on "mobile robotics" with the term "agriculture 4.0" in the title, abstract or keywords. Buildings The rapid development of the construction industry has highlighted the urgent need for enhanced construction efficiency and safety, propelling the development of construction robots to ensure sustainable and intelligent industry advancement. Welding robots, in particular, hold significant promise for application in steel structure construction. However, harsh construction environments ... Swarm Intelligence in Robotics: Principles, Applications, and Future Swarm intelligence (SI) is an innovative field in robotics inspired by the collective behavior of social insects like ants, bees, and birds. It involves multiple robots working collaboratively to achieve complex tasks that would be difficult for a single robot to accomplish alone. This article delves into the principles underlying swarm intelligence, its applications in various domains, the ... (PDF) The future of Robotics Technology This paper provides an overview about robotics and its various applications useful for healthcare. Significant enhancement, quality services, and advancements in healthcare services are also ... Simulation research on Tai Chi movement posture resolution ... The combined system based on multiple MEMS sensors is a miniature measurement system used for dynamic output and display of 3D information about the user's posture. It is mainly used for various Tai Chi movement posture calculation simulation research, wearable devices, etc. This article explores MEMS sensor technology, focusing on MEMS sensor data processing, Tai Chi movement position ... Utilisation of Robotic Systems in Automating Manufacturing Applications In addition, this paper tries to analyze the collected data and also shines a light on the future application of robotics and its outcomes in the manufacturing industry. This paper has preferred the secondary qualitative process for the doing entire research work and it can be said that this is one of the smooth ways to get outstanding outcomes ... Nanorobotic Applications in Medicine: Current Proposals and Designs The goal of this paper is to give an introduction to the emerging field of nanorobotics within medicine, and provide a review of the emerging applications of nanorobotics to fields ranging from neurosurgery to dentistry. ... There is a rich base of research and potential applications for nanomedicine and nanorobotic applications in the field of ... (PDF) Robots and Their Applications Service robots, on the other hand, assist humans. in their tasks. These include chores at home like vacuum clears, transportation like. self-driving cars, and defense applications such as ... Utilization of Automation and Robotics in Pharmacy Practice Automation and robotics have become an integral part of various industries, including healthcare. In pharmacy practice, these technologies play a crucial role in improving efficiency, accuracy, and safety of medication dispensing and management processes. This essay explores the implications of automation and robotics in pharmacy practice at the Master level, highlighting their benefits ... Essays and Short Answer Prompts The Penn application process includes a personal essay—which is sent to most schools you apply to—as well as a few short answer prompts.We read your words carefully, as they are yet another window into how you think, what you value, and how you see the world. (PDF) About robot applications and robotic research in cooperative The new trends in robotics research have been denominated service robotics because of their general goal of getting robots closer to human social needs, and this article surveys research on ... (PDF) Research Paper on Robotics-New Era Student. , M.Sc. I.T., I.C.S. College, Khed, Ratnagri. Abstract: This paper contains of detailed statistics about the robot's method and system. As one and all knows, how artificial. intelligence ... assignment business plan case study course work essay report research paper speech term paper thesis