Shared autonomy functions as a flexible framework that empowers robots to operate across a spectrum of autonomy levels, allowing for efficient task execution with minimal human oversight. However, humans might be intimidated by the autonomous decision-making capabilities of robots due to perceived risks and a lack of trust. This paper proposed a trust-preserved shared autonomy strategy that allows robots to seamlessly adjust their autonomy level, striving to optimize team performance and enhance their acceptance among human collaborators. By enhancing the relational event modeling framework with Bayesian learning techniques, this paper enables dynamic inference of human trust based solely on time-stamped relational events communicated within human-robot teams. Adopting a longitudinal perspective on trust development and calibration in human-robot teams, the proposed trust-preserved shared autonomy strategy warrants robots to actively establish, maintain, and repair human trust, rather than merely passively adapting to it. We validate the effectiveness of the proposed approach through a user study on a human-robot collaborative search and rescue scenario. The objective and subjective evaluations demonstrate its merits on both task execution and user acceptability over the baseline approach that does not consider the preservation of trust.

翻译：共享自主作为一种灵活框架，使机器人能够在不同自主等级间运行，以最少的人类监督实现高效任务执行。然而，由于感知风险与信任缺失，人类可能会对机器人的自主决策能力产生抵触心理。本文提出一种信任保持型共享自主策略，使机器人能够无缝调节自主等级，致力于优化团队绩效并提升人类协作者的接受度。通过将贝叶斯学习技术融入关系事件建模框架，本文仅依据人机团队中传递的时间戳关系事件，即可实现对人类信任的动态推断。本文采用纵向视角审视人机团队中信任的建立与校准过程，所提出的信任保持型共享自主策略使机器人能够主动建立、维护和修复人类信任，而非单纯被动适应信任变化。通过人机协作搜索救援场景的用户研究，我们验证了该方法的有效性。客观与主观评估表明，相较于未考虑信任保持的基线方法，本方法在任务执行效率与用户接受度方面均具有显著优势。