Human multi-robot system (MRS) collaboration is demonstrating potentials in wide application scenarios due to the integration of human cognitive skills and a robot team's powerful capability introduced by its multi-member structure. However, due to limited human cognitive capability, a human cannot simultaneously monitor multiple robots and identify the abnormal ones, largely limiting the efficiency of the human-MRS collaboration. There is an urgent need to proactively reduce unnecessary human engagements and further reduce human cognitive loads. Human trust in human MRS collaboration reveals human expectations on robot performance. Based on trust estimation, the work between a human and MRS will be reallocated that an MRS will self-monitor and only request human guidance in critical situations. Inspired by that, 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), meanwhile accelerates the convergence speed by integrating active learning to reduce human workload. To validate the effectiveness of the method, tasks "searching victims in the context of city rescue" were designed in an open-world simulation environment, and a user study with 10 volunteers was conducted to generate real human trust feedback. The results showed that by maximally utilizing human feedback, the STL achieved higher accuracy in trust modeling with a few human feedback, effectively reducing human interventions needed for modeling an accurate trust, therefore reducing human cognitive load in the collaboration.
翻译:人类多机器人系统(MRS)的协作正在广泛应用情景中显示出潜力,因为人类认知技能一体化和机器人团队的强大能力是由多成员结构引入的,然而,由于人类认知能力有限,人类不能同时监测多个机器人并查明异常机器人,这在很大程度上限制了人类-MRS合作的效率;迫切需要积极主动地减少不必要的人类参与,并进一步减少人类认知负荷;人类对人的多机器人系统合作的信任暴露了人类对机器人性能的期望;根据信任估计,人类与机器人团队之间的工作将重新分配,即MRS将自我监测,只要求人类在危急情况下提供指导;由于这一点,人类认知能力有限,人类不能同时监测多个机器人并查明异常机器人,这在很大程度上限制了人类-MRS合作的效率; 迫切需要积极主动地减少人类信任的两个方面(信任水平和信任偏好),同时通过将积极学习与减少人类工作量相结合,加快人类对合并速度; 为了验证方法的有效性,在开放世界模拟环境中设计了“在城市救援背景下搜寻受害者”的任务,只要求人类在关键情况下获得指导; 受此启发的用户研究,以10个新的合成人际的准确性反馈,通过人类信任来有效减少人类信心,在人类信任中显示人类的难度分析。