Considerations for Task Allocation in Human-Robot Teams

In human-robot teams where agents collaborate together, there needs to be a clear allocation of tasks to agents. Task allocation can aid in achieving the presumed benefits of human-robot teams, such as improved team performance. Many task allocation methods have been proposed that include factors such as agent capability, availability, workload, fatigue, and task and domain-specific parameters. In this paper, selected work on task allocation is reviewed. In addition, some areas for continued and further consideration in task allocation are discussed. These areas include level of collaboration, novel tasks, unknown and dynamic agent capabilities, negotiation and fairness, and ethics. Where applicable, we also mention some of our work on task allocation. Through continued efforts and considerations in task allocation, human-robot teaming can be improved.Comment: Presented at AI-HRI symposium as part of AAAI-FSS 2022 (arXiv:2209.14292

The Effect of Entrepreneurial Orientation and Organisational Culture on Firm Performance: The Mediating Role of Innovation

This study is concerned with conceptual model development, which examines the innovation influence on the relationship of organisational culture, entrepreneurial orientation and performance of large manufacturing firms in Pakistan. A total of 399 questionnaires were distributed to large manufacturing firms in Pakistan to assess the relationships between organisational culture, entrepreneurial orientation, innovation, and firm performance. The findings reveal that entrepreneurial orientation significantly influences the performance of large manufacturing firms in Pakistan. Meanwhile, the results also indicated that other factors such as organisational culture do not significantly relate to the manufacturing firm’s performance. Interestingly, the results had shown that the factor of innovation significantly mediated the relationships between organisational culture, entrepreneurial orientation and firm performance. Based on the results, it can be summarised that the mechanism used to enhance the innovative culture in the organisation will also contribute to the introduction, adoption and diffusion of innovations. Additionally, these factors would contribute to increased performance and achievement in the near future. Indeed, this statement is supported by numerous current and past studies conducted in Pakistan, where scholars found that innovation is the primary impetus of firm performance. However, there exists a very limited number of studies regarding the impact of innovation on organisational culture and entrepreneurial orientation in manufacturing firms in Pakistan. This study would lead to a significant contribution to the prevailing literature by empirically examining the relationship between organisational culture, entrepreneurial orientation, innovation and firm performance

Vipassana Sebagai Upaya Preventif Bagi Penderita Fear Of Missing Out (FoMo)

Penelitian ini menggunakan rancangan eskperimen two gruop yang bertujuan untuk melihat pengaruh meditasi vipasnaa terhadap fear of missing out. Subjek pada penelitian ini adalah 30 mahasiswi yang mengalami fear of missing out dalam kategori parah. Subjek penelitian ini selanjutnya dibagi dalam dua kelompok yaitu 15 mahasiswi yang diberikan meditasi vippasana dan 15 mahasiswi lainnya diberikan materi meditasi dasar. Metode pengumpulan data menggunakan skala fear of missing out. Teknik analiasa data menggunakan uji paired sample t test. Hasil penelitian ini menunjukan bahwa terdapat penurunan tingkat fear of missing out pada mahasiswa mendapatkan perlakuan meditasi vipassana

Using Trust for Heterogeneous Human-Robot Team Task Allocation

Human-robot teams have the ability to perform better across various tasks than human-only and robot-only teams. However, such improvements cannot be realized without proper task allocation. Trust is an important factor in teaming relationships, and can be used in the task allocation strategy. Despite the importance, most existing task allocation strategies do not incorporate trust. This paper reviews select studies on trust and task allocation. We also summarize and discuss how a bi-directional trust model can be used for a task allocation strategy. The bi-directional trust model represents task requirements and agents by their capabilities, and can be used to predict trust for both existing and new tasks. Our task allocation approach uses predicted trust in the agent and expected total reward for task assignment. Finally, we present some directions for future work, including the incorporation of trust from the human and human capacity for task allocation, and a negotiation phase for resolving task disagreements.Army Research Lab under grant #F061352National Science FoundationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/170403/1/Ali et al. 2021 AAAI 2020 Post.pdfDescription of Ali et al. 2021 AAAI 2020 Post.pdf : ArticleSEL

Considerations for Task Allocation in Human-Robot Teams

In human-robot teams where agents collaborate together, there needs to be a clear allocation of tasks to agents. Task allocation can aid in achieving the presumed benefits of human-robot teams, such as improved team performance. Many task allocation methods have been proposed that include factors such as agent capability, availability, workload, fatigue, and task and domain-specific parameters. In this paper, selected work on task allocation is reviewed. In addition, some areas for continued and further consideration in task allocation are discussed. These areas include the level of collaboration, novel tasks, unknown and dynamic agent capabilities, negotiation and fairness, and ethics. Where applicable, we also mention some of our work on task allocation. Through continued efforts and considerations in task allocation, human-robot teaming can be improved.Army Research Office Cooperative Agreement Number W911NF-21-2-0168Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/174994/1/AI_HRI_2022_final.pdfDescription of AI_HRI_2022_final.pdf : Main FileSEL

Toward Personalized Tour-Guide Robot: Adaptive Content Planner based on Visitor’s Engagement

In the evolving landscape of human-robot interactions, tour-guide robots are increasingly being integrated into various settings. However, the existing paradigm of these robots relies heavily on prerecorded content, which limits effective engagement with visitors. We propose to address this issue of visitor engagement by transforming tour-guide robots into dynamic, adaptable companions that cater to individual visitor needs and preferences. Our primary objective is to enhance visitor engagement during tours through a robotic system capable of assessing and reacting to visitor preferences and engagement. Leveraging this data, the system can calibrate and adapt the tour-guide robot’s content in real-time to meet individual visitor preferences. Through this research, we aim to enhance the tour-guide robots’ impact in delivering engaging and personalized visitor experiences by providing an adaptive tour-guide robot solution that can learn from humans’ preferences and adapt its behaviors by itself.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/192072/1/Lin et al. 2024.pdfDescription of Lin et al. 2024.pdf : Published VersionSEL

Promises and Trust Repair in UGVs

Unmanned ground vehicles (UGVs) are autonomous robots capable of performing tasks through self navigation and decision-making. They have the potential to replace humans in dangerous driving scenarios. However, UGVs must be viewed as trustworthy to be accepted, and like any automation, they can make mistakes that decrease human trust in them. Trust repair strategies can mitigate the consequences of trust violations, but they are not always effective. To better understand their effectiveness on UGVs, we designed a between-subjects study examining promises on a UGV’s trustworthiness. Preliminary results showed that promises had a marginal impact on overall trustworthiness but were influential in repairing benevolence but not ability or integrity. These findings have implications for the design of UGVs and trust repair theory.Automotive Research Center (ARC) in accordance with Cooperative Agreement W56HZV-19-2-0001 U.S. Army DEVCOM Ground Vehicle Systems Center (GVSC) Warren, MI.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/177382/1/Esterwood et al. HFES_2023.pdfDescription of Esterwood et al. HFES_2023.pdf : PreprintSEL