  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.
