Toward Seamless Human-Robot Collaboration: VR-Enhanced Teleoperation with Transparent Shared Control

Shared control is widely used in robotic telemanipulation
to combine human input with autonomous assistance. However, assis-
tive behaviors are often embedded within the control loop and remain
difficult for operators to interpret, leading to potential misalignment be-
tween user intent and system response. To resolve this misalignment,
we propose an immersive teleoperation system based on shared control,
integrating virtual fixtures with explicit visual cues that expose assis-
tive behavior during task execution. The system is evaluated through a
controlled user study (N = 10) on a constrained pick-and-place task, as-
sessing task performance, cognitive workload, and trust. Results demon-
strate that transparency significantly improves trajectory accuracy and
motion stability in constrained scenarios when combined with assistance,
enabling more structured and anticipatory interaction. These findings in-
dicate that transparency enhances the operator’s ability to interpret and
regulate assistance behaviors, supporting more efficient human-robot co-
ordination. While subjective measures remain comparable, the observed
interaction patterns suggest that transparency primarily operates at the
behavioral level by facilitating mental model formation and improving
the explainability of shared control, which is otherwise perceived as a
black-box process.