Human Collaboration Research Articles

Design and assembly automation of the Robotic Reversible Timber Beam

The field of architecture is more and more referring to wood as a sustainable solution for satisfying the need of new buildings while offsetting carbon emissions. In parallel, the research in Robotic Timber Construction (RTC) has evolved rapidly, focusing both on fabrication and assembly processes in order to improve the productivity of the construction industry and extend the production digital chain. This paper presents an original workflow for the automation from design to layered robotic assembly of reversible timber structures, aided by human-robot collaboration. An advanced digital design workflow for non-standard timber structures is established, shifting the focus towards the assembly of a kit of discrete wood elements into larger configurations that can be reconfigured in time. Linked to this, collaborative robots are employed for grasping, positioning and fixing of discrete timber elements, with the introduction of a multi-phase end effector that connects wood elements through reversible joinery. As such, the work introduces fundamental design and manufacturing concepts for an automated construction process that extends the material life cycle of wood, and consequently, its carbon store. The enhancement of robotic assembly is here achieved with the (1) integration of a feedback system based on location and force signals, and (2) human collaboration to provide immediate assistance to the robot in the case the signals do not match the defined assembly conditions. The various developments are applied to a research demonstrator which crystallizes the various design, constructional, and manufacturing inputs into a physical output, which is discussed from multiple viewpoints, in order to delineate relevant areas for future studies.

Artificial Intelligence and a Microperspective of Organizational Corruption

Building on previous research on the collaborative root of corruption, we propose and conclude that people cheat more when they collaborate with another person in comparison with an artificial intelligence (AI) robot. We suggest that people infer that a human collaborator would cheat more than an AI collaborator when working together. Having drawn this ethical inference about a collaborator’s behavior, people are more likely to cheat subsequently in human-human collaboration than in human-AI collaboration. Therefore, the ethical inference about a collaborator’s behavior is the reason why people are more inclined to cheat in human-human collaboration than human-AI collaboration. We tested our theory in two lab experiments. Additionally, in two online experiments, we showed our findings were counterintuitive with regard to the common belief of laypeople. Managers are unaware of the positive effect of human-AI collaboration on reducing cheating behavior in organizations. Our research contributes to the microperspective of organizational corruption and has implications for anticorruption and compliance policies in organizations and society as a whole.

Innovation Practices Using AI technology: Exploring Human-Machine Knowledge Conversion

The use of AI in innovation practices creates a conundrum for knowledge creation theory. Current AI technology possesses the ability to engage with data in ways that differ from the original design due to their response to contextual input data. AI may be said to embody tacit knowledge as it evolves and learns about the human collaborator and the organisational and technological context. We explore knowledge conversion involving human and AI to understand the process of digital innovation starting from the mutually constituted innovation agency. We articulate a process model of knowledge creation extending Nonaka’s seminal SECI model starting with a sociomaterial lens on knowledge conversion and a recursive relationship between the outcome of knowledge creation and the material interactions of human and AI. Our analysis links characteristics of digital innovation with the potential of AI to perform skilful work or, in other words, appear to act on tacit knowledge. This insight contributes to the literature on digital innovation a missing link for seeing material agency as intermingled with human agency and to organizational knowledge creation theory the possibility of the machine becoming part of a team. We discuss theoretical and practical implications of our study for the economics of digital innovation and suggest avenues for future research.

Human wealth evolution: Trends and fluctuations

Is a causal description of human wealth history conceivable? To investigate the matter we introduce a simple causal albeit strongly aggregated model, assuming that the observed wealth growth is mainly driven by human collaborative efforts whose intensity itself increases with increasing wealth. As an empirical reference we use time series describing eight centuries of per capita annual gross domestic products (GDP) of three European countries, the UK, France and Sweden. The model requires a population large enough for disruptive events, e.g. famine, epidemics and wars, not to destroy the fundamental workings of society. Cultural interchanges between different geographical areas are not explicitly taken into account. The wealth evolution trend can then be described by an ordinary differential equation with three free parameters: one producing a short term exponential growth rate, one defining an additional minute constant growth rate and finally one that specifies the time scale below which human collaboration intensity can be treated as constant. Beyond that scale, wealth enhances the fundamental collaborative infrastructure of society. The solution features a finite time singularity, which implies infinite GDP per capita in finite time and thus suggests a lack of long term sustainability. The year at which the singularity occurs has a slight variation near 2020 AD from one country to another. GDP time series curtailed after 1900 AD produce similar values for the occurrence of the singularity, which thus could be predicted more than hundred years ago. Curtailing the GDP series from the early years up to 1700 AD also produces stable and consistent predictions for the singularity time. Power spectra are obtained for de-trended data spanning eight centuries, as well as for the first and last four centuries of the same period. All spectra have an overall signature where the power decays as the inverse frequency squared. The embedded peaks are reminiscent of the cycles described in the economic literature, but are also present in time series far predating industrialization. The background fluctuations in the GDP series is tentatively interpreted as societal response to disruptive stochastic events. e.g. new economic activities following epochal discoveries, as well as wars and epidemics.

Artist-led building: farming organic knowing

ABSTRACT This article presents a writing collaboration between an ethnographer and two artists. It was developed from a one-week residency at Kultivator, which is an artist-led project situated on an organic farm on the Swedish island of Öland. The writing is informed by classical pragmatist philosophy and gives focus to the organic trope of human-environment continuity. Drawing on the writing experiment the article argues that Kultivator is not simply doing organic farming; but building a farm to think-with organically. Kultivator is presented as a way of knowing continuity; and a way of doing organic philosophy also. As such the article suggests this artist-led practice has capacity to stretch artistic practice and its discourse beyond human collaboration. The research contributes an experiential account of artist-led practice in a rural context. Rather than focusing on artworks or ‘buildings’ this collaboration asks us to consider the way we ‘build’ our participatory process of living together and the role artistic knowledge can have in doing so.

Learn How to Assist Humans Through Human Teaching and Robot Learning in Human–Robot Collaborative Assembly

Human–robot collaborative assembly has been one of the next-generation manufacturing paradigms in which superiorities of humans and robots can be fully leveraged. To enable robots effectively collaborate with humans, similar to human–human collaboration, robot learning from human demonstrations has been adopted to learn the assembly tasks. However, existing feature-based approaches require critical feature design and extraction process and are usually complex to incorporate task contexts. Existing learning-based approaches usually require a large amount of manual effort for data labeling and also rarely consider task contexts. This article proposes a dual-input deep learning approach to incorporate task contexts into the robot learning from human demonstration process to assist human in assembly. In addition, online automated data labeling during human demonstration is proposed to reduce the training efforts for learning. The experimental validations on a realistic human–robot model car assembly task with safety-concerned execution designs demonstrate the effectiveness and advantages of the proposed approaches.

On the relationship between network connectivity and group performance in small teams of humans: experiments in virtual reality

Optimizing group performance is one of the principal objectives that underlie human collaboration and prompts humans to share resources with each other. Connectivity between individuals determines how resources can be accessed and shared by the group members, yet, empirical knowledge on the relationship between the topology of the interconnecting network and group performance is scarce. To improve our understanding of this relationship, we created a game in virtual reality where small teams collaborated toward a shared goal. We conducted a series of experiments on 30 groups of three players, who played three rounds of the game, with different network topologies in each round. We hypothesized that higher network connectivity would enhance group performance due to two main factors: individuals’ ability to share resources and their arousal. We found that group performance was positively associated with the overall network connectivity, although registering a plateau effect that might be associated with topological features at the node level. Deeper analysis of the group dynamics revealed that group performance was modulated by the connectivity of high and low performers in the group. Our findings provide insight into the intricacies of group structures, toward the design of effective human teams.

Zooming out of the crisis: Language and human collaboration

AbstractLanguage teachers are often masters of using the physical space in their language classrooms, rearranging furniture, groups, and artifacts to facilitate meaningful encounters with and among learners. Indeed, during the COVID‐19 crisis, many language teachers are sharing that these human encounters—reading learners' needs through body language, moving in and out of conversations, or engaging each other face‐to‐face—are the biggest felt loss in their emergent digital language classrooms. Yet, the new digital realities do not necessarily mean that teachers must sacrifice real collaboration among their learners. Digital tools were often designed to explicitly facilitate multimodal collaboration, and, with a wider variety of humans and human stories than may be found within the four walls of the typical classroom. This article aims to help language teachers facilitate more diverse interactions in the target language through the use of tools, spaces, and strategies that can be easily incorporated into our digital classrooms. We describe three categories of such activities (mobile‐assisted learning, tandem learning, and service‐learning) and explain how these can be most effectively incorporated into an online language class.

Fabrication of Advanced Agribot Robot

Numerous nations in Asia including India are agrarian economies and the majority of their rustic populaces rely upon horticulture to acquire their business. The utilization of apparatuses and domesticated animals in the rural procedure has decreased the human exertion. Central point that influence agribusiness incorporate less holding territory, deficiency of seeds, manures and work and vulnerability of storm. The motorization of horticulture alludes to the utilization of devices or machines in the rural procedure that conceivably lessens the human exertion. In spite of the fact that it lessens the human exertion in the horticultural procedure, it requires total human collaboration. The mechanization and apply autonomy application in the part of agribusiness is at the blasting stage when contrasted with its wide scope of use in different divisions. Numerous inquires about have been done in this field to robotize the procedure. In the current paper an exertion is made for the plan and advancement of the robot that can perform seeding process with no human intercession. The robot created is equipped for causing a gap in the dirt to up to certain profundity, putting the seed precisely in a similar gap and shutting the mud. The procedure is constrained by a microcontroller. The robot created defeats the disadvantages in the customary technique for seeding which incorporates wastage of seeds, high work wage, lower use of land and so forth. By the utilization of computerization and mechanical technology in the field of farming it is conceivable to build the general proficiency of the agrarian procedure and can alleviate impacts of work lack. This paper likewise presents the IoT-based horticultural creation framework for balancing out organic market of rural items while building up the earth sensors and expectation framework for the development and creation measure of yields by social affair its ecological data. As of now, the interest by utilization of horticultural items could be anticipated quantitatively, be that as it may, the variety of reap and creation by the difference in homestead's developed zone, climate change, infection and bug harm and so on. Couldn't be anticipated, so the market interest of agrarian items has not been controlled appropriately. To beat it, this paper structured the IoT-based checking framework to dissect crop condition, and the technique to improve the effectiveness of dynamic by investigating harvest measurements.

West Nile virus in Europe: after action reviews of preparedness and response to the 2018 transmission season in Italy, Slovenia, Serbia and Greece

BackgroundAfter Action Reviews (AAR) with a One Health perspective were performed in Slovenia, Italy, Serbia and Greece following a severe West Nile virus (WNV) transmission season in 2018. A protocol combining traditional techniques and organizational process analysis was developed and then implemented in each country.ResultsIn 2018, response to the unusually intense transmission season of WNV in Slovenia, Italy, Serbia and Greece took place through routine response mechanisms. None of the four countries declared a national or subnational emergency. We found a very strong consensus on the strengths identified in responding to this event. All countries indicated the availability of One Health Plans for surveillance and response; very high laboratory diagnostic capacity in the human, veterinary and entomology sectors and strong inter-sectoral collaboration with strong commitment of engaged institutions as critical in the management of the event. Finally, countries implementing One Health surveillance for WNV (in terms of early warning and early activation of prevention measures) consistently reported a positive impact on their activities, in particular when combining mosquito and bird surveillance with surveillance of cases in humans and equids. Recurring priority areas for improvement included: increasing knowledge on vector-control measures, ensuring the sustainability of vector monitoring and surveillance, and improving capacity to manage media pressure.ConclusionsThe AARs presented here demonstrate the benefit of cross-sectoral and cross-disciplinary approaches to preparedness for West Nile virus outbreaks in Europe. In the coming years, priorities include fostering and strengthening arrangements that: enable coordinated One Health surveillance and response during WNV transmission seasons; ensure adequate laboratory capacities; strengthen risk communication; and fund longer-term research to address the knowledge gaps identified in this study.

A constructive model for collective intelligence.

SummaryNatural phenomena of collective intelligence (CI) occurring in physical space show a potential approach to effective large-scale human collaboration in cyberspace. Based on existing explanatory understanding of CI, this perspective proposes a constructive model for building artificial CI systems, i.e., problem-oriented CI phenomena with AI-powered information integration and feedback.

A Framework for the Image Retrieval System Based on Histogram Normalization Technique with Python

The processing of multimedia content is used for real-world computer vision in various applications, and digital images make up a large part of multimedia data. The processing of multimedia content is used for real-computer vision in various applications, and digital images make up a large part of multimedia data. Content-based Retrieval of photographs (CBIR) is a system of picture recovery which utilizes the visual highlights of a picture, for example, shading, shape and surface so as to look through the client based inquiry pictures from the huge databases. CBIR relies upon highlight extraction of a picture which are the visual highlights and these highlights are extricated naturally i.e. without human collaboration. We intend in this paper to provide a detailed overview of recent developments related to CBIR and image representation. We researched the main aspects of various models of image recovery and image representation from low-level feature extraction to recent semantict ML approaches. And, for extraction of features, HSV, image segmentation and color histogram techniques are used, which effectively gives us the main point in an image that these techniques are used to minimize complexity, expense, and energy and time consumption. Then a machine learning model is trained for similarity test and the validation and texting phases are performed accordingly which leads to better performance as. Then a machine learning model is trained for similarity testing and then the validation and texting steps are performed accordingly, resulting in improved results compared to previously performed techniques. The precision values in the proposed technique are fairly excellent.

The influence of objects on creativity

This study is an examination of how objects can influence creativity. Through a qualitative inquiry involving 22 consumers, the study sheds light on how objects can inform, inspire, and facilitate creativity. Specifically, the findings show that objects can physically inform and facilitate the creative process because of their material properties (i.e., colors, densities, masses, lengths, and shapes) and their embeddedness in object–human interaction networks. Although object agency is initially latent and located in the materiality of objects, it is unlocked during object–human interactions, ultimately enhancing creativity. The findings highlight an overlooked role of objects in creative processes. Further, this study contributes to the existing corpus of creativity research, which attributes creativity mostly to human agency, as rooted in human personality, human social systems, and human collaborations. The study also extends and supports object agency and service‐dominant logic discourses.

Land and ethnographic practices—(re)making toward healing

ABSTRACT While ‘place-making’ is a productive concept for many scholars, I consider land relations and research through Indigenous (re)making by narrating embodied and experiential ethnographic approaches. Through (re)making, one reimagines ethnography and ethnographic practices with Indigenous land and Indigenous land practices that work to contribute to forms of decolonial healing and Indigenous survivance. In consideration of the wounds among multiple bodies, not only humans, for which large-scale settler-colonial agricultural plantations in Hawai’i and the Sonoran Desert are responsible, this work discusses possibilities for (re)making toward healing with land. Song, poetry, and story are the agents who make such engagements with many more-than-(but including)-human collaborators translatable. As such, it asks how an understanding of ethnography in the contexts of Indigenous science and technology studies that cares about environmental research might shift the ways in which methodologies are formed and ‘data’ is shaped. And it asks how ethnographic practitioners might better affirm the plights, journeys, cycles, relations, and encounters that more-than-human beings experience, remember, hold, and live. This is one contribution among many that attend to the variety of ways in which ethnographic texts are formed and understood in Indigenous research practices that challenge Euro-Western genres and structures of knowledge production, and how/with whom such knowledges and practices are co-created.

Learning to Communicate Implicitly by Actions

In situations where explicit communication is limited, human collaborators act by learning to: (i) infer meaning behind their partner's actions, and (ii) convey private information about the state to their partner implicitly through actions. The first component of this learning process has been well-studied in multi-agent systems, whereas the second — which is equally crucial for successful collaboration — has not. To mimic both components mentioned above, thereby completing the learning process, we introduce a novel algorithm: Policy Belief Learning (PBL). PBL uses a belief module to model the other agent's private information and a policy module to form a distribution over actions informed by the belief module. Furthermore, to encourage communication by actions, we propose a novel auxiliary reward which incentivizes one agent to help its partner to make correct inferences about its private information. The auxiliary reward for communication is integrated into the learning of the policy module. We evaluate our approach on a set of environments including a matrix game, particle environment and the non-competitive bidding problem from contract bridge. We show empirically that this auxiliary reward is effective and easy to generalize. These results demonstrate that our PBL algorithm can produce strong pairs of agents in collaborative games where explicit communication is disabled.

Towards Efficient Human-Robot Collaboration With Robust Plan Recognition and Trajectory Prediction

Human-robot collaboration (HRC) is becoming increasingly important as the paradigm of manufacturing is shifting from mass production to mass customization. The introduction of HRC can significantly improve the flexibility and intelligence of automation. To efficiently finish tasks in HRC systems, the robots need to not only predict the future movements of human, but also more high-level plans, i.e., the sequence of actions to finish the tasks. However, due to the stochastic and time-varying nature of human collaborators, it is quite challenging for the robot to efficiently and accurately identify such task plans and respond in a safe manner. To address this challenge, we propose an integrated human-robot collaboration framework. Both plan recognition and trajectory prediction modules are included for the generation of safe and efficient robotic motions. Such a framework enables the robots to perceive, predict and adapt their actions to the human's work plan and intelligently avoid collisions with the human. Moreover, by explicitly leveraging the hierarchical relationship between plans and trajectories, more robust plan recognition performance can be achieved. Physical experiments were conducted on an industrial robot to verify the proposed framework. The results show that the proposed framework could accurately recognize the human workers' plans and thus significantly improve the time efficiency of the HRC team even in the presence of motion classification noises.

Dynamic Houses and Communities at Çatalhöyük: A Building Biography Approach to Prehistoric Social Structure

Houses are rich resources for understanding prehistoric social structure. However, conventional working methods often handle houses as stable entities that reflect the nature of households and other social units. Social groups may be inadvertently rendered static in the process. A biographical understanding, in which the on-going transformation of built space is part of different kinds of human collaboration, allows us to explore the dynamic qualities of past communities. I examine detailed life-histories of four contemporary houses at Çatalhöyük in Central Turkey, using the site's fine stratigraphy to interrogate how furnishings, elaborations and rhythms of burial varied through each building's use-life. These trace shifting practice and performance in relation to built space. Çatalhöyük buildings’ social roles changed dramatically over their lives. The spatial dynamics observed suggest that commensal groups were less stable and less bound to specific houses than in more conventional views of the site, and interacted in unpredictable ways with larger forms of social collaboration. Ultimately, this suggests a more dynamic approach to both houses and social units in the Near East and the archaeology of houses generally.

Human-in-the-Loop Robotic Manipulation Planning for Collaborative Assembly

This article develops a robotic manipulation planner for human–robot collaborative assembly. Unlike previous methods that study an independent and fully artificial intelligent (AI)-equipped autonomous system, this article explores the subtask distribution between a robot and a human and studies a human-in-the-loop robotic system for collaborative assembly. The system distributes the subtasks of an assembly to robots and humans by exploiting their advantages and avoiding their disadvantages. The robot in the system will work on pick-and-place tasks and provide workpieces to humans. The human collaborator will work on fine operations, such as aligning, fixing, and screwing. A constraint-based incremental manipulation planning method is proposed to generate the motion for the robots. The performance of the proposed system is demonstrated by asking a human and the dual-arm robot to collaboratively assemble a cabinet. The results show that the proposed system and planner are effective and efficient and can assist humans in finishing the assembly task comfortably. Note to Practitioners —This article was motivated by assembling a cabinet. The assembling process involves several pick-and-place, reorientation, regrasp, alignment, peg-in-hole, and screwing subtasks. The system distributes these subtasks to robots and humans by exploiting their advantages and avoiding their disadvantages. The robot used is a dual-arm robot with two parallel grippers. Thus, a suction cup tool is used for the pick-and-place of thin objects. Soft-finger contact constraints are considered to assure safe manipulation. Human ergonomics are included as a quality for optimizing the handover between robot arms and robot and human. The proposed planner and system show satisfying user experience and expedite the cabinet assembly. It is expected to help relax the labor-intensive manufacturing process in the future.

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

Machines incorporating techniques from artificial intelligence and machine learning can work with human users on a moment-to-moment, real-time basis to generate creative outcomes, performances and artefacts. We define such systems collaborative, creative AI systems, and in this article, consider the theoretical and practical considerations needed for their design so as to support improvisation, performance and co-creation through real-time, sustained, moment-to-moment interaction. We begin by providing an overview of creative AI systems, examining strengths, opportunities and criticisms in order to draw out the key considerations when designing AI for human creative collaboration. We argue that the artistic goals and creative process should be first and foremost in any design. We then draw from a range of research that looks at human collaboration and teamwork, to examine features that support trust, cooperation, shared awareness and a shared information space. We highlight the importance of understanding the scope and perception of two-way communication between human and machine agents in order to support reflection on conflict, error, evaluation and flow. We conclude with a summary of the range of design challenges for building such systems in provoking, challenging and enhancing human creative activity through their creative agency.

Dancing With Algorithms: Interaction Creates Greater Preference and Trust in Machine-Learned Behavior.

We examined a method of machine learning (ML) to evaluate its potential to develop more trustworthy control of unmanned vehicle area search behaviors. ML typically lacks interaction with the user. Novel interactive machine learning (IML) techniques incorporate user feedback, enabling observation of emerging ML behaviors, and human collaboration during ML of a task. This may enable trust and recognition of these algorithms. Participants judged and selected behaviors in a low and a high interaction condition (IML) over the course of behavior evolution using ML. User trust in the outputs, as well as preference, and ability to discriminate and recognize the behaviors were measured. Compared to noninteractive techniques, IML behaviors were more trusted and preferred, as well as recognizable, separate from non-IML behaviors, and approached similar performance as pure ML models. IML shows promise for creating behaviors by involving the user; this is the first extension of this technique for vehicle behavior model development targeting user satisfaction and is unique in its multifaceted evaluation of how users perceived, trusted, and implemented these learned controllers. There are many contexts where the brittleness of ML cannot be trusted, but the advantage of ML over traditional programmed behaviors may be large, as in some military operations where they could be scaled. IML in this early form appears to generate satisfactory behaviors without sacrificing performance, use, or trust in the behavior, but more work is necessary.