Human multi-robot system (MRS) collaboration is demonstrating potentials in wide application scenarios . A novel Synthesized Trust Learning (STL) method was developed to model human trust in the collaboration . STL explores two aspects of human trust (trust level and trust preference) and accelerates convergence speed by integrating active learning to reduce workload . The STL achieved higher accuracy in trust modeling with a few human feedback, effectively reducing human interventions needed formodeling an accurate trust, therefore reducing human cognitive load in the collaborative process . The results showed that by maximallyutilizing human feedback,. the STL achieved . higher accuracy . in . trust modeling, effectively reduced human interventions, effectively . reducing human . interventions needed to formodeled an accurateTrust, therefore reduce human . engagements and further reduce human cognitive loads in the . collaboration, therefore reduces human interactions, the researchers said. The researchers said . The researchers say. The research was published in the open-worldsimulation environment, and a user study with 10 volunteers was conducted togenerate real human trust feedback. The results were conducted to .

Author(s) : Yijiang Pang, Chao Huang, Rui Liu

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Keywords : human - trust - feedback - stl - learning -

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