Effects of Shared Control on Cognitive Load and Trust in Teleoperated Trajectory Tracking

TL;DR

The paper investigates how varying robot autonomy in a shared-control teleoperation task affects a user’s cognitive load and trust. Using a blending controller and a dual-task trajectory-tracking paradigm, the study finds that higher autonomy reduces subjective cognitive load and increases trust, yet objective and physiological measures do not consistently reflect the same load reductions, and no robust three-way interaction among autonomy, cognitive load, and trust is detected. Results show improved tracking with higher autonomy and a strong alignment between MDMT trust and a single trust item, suggesting transparency and performance influence trust. The work highlights the need for continuous autonomy levels and robust, multi-time-scale cognitive-load assessment to inform the design of adaptable assistive robotic systems.

Abstract

Teleoperation is increasingly recognized as a viable solution for deploying robots in hazardous environments. Controlling a robot to perform a complex or demanding task may overload operators resulting in poor performance. To design a robot controller to assist the human in executing such challenging tasks, a comprehensive understanding of the interplay between the robot's autonomous behavior and the operator's internal state is essential. In this paper, we investigate the relationships between robot autonomy and both the human user's cognitive load and trust levels, and the potential existence of three-way interactions in the robot-assisted execution of the task. Our user study (N=24) results indicate that while the autonomy level influences the teleoperator's perceived cognitive load and trust, there is no clear interaction between these factors. Instead, these elements appear to operate independently, thus highlighting the need to consider both cognitive load and trust as distinct but interrelated factors in varying the robot autonomy level in shared-control settings. This insight is crucial for the development of more effective and adaptable assistive robotic systems.

Figures (4)

• Figure 1: Experiment setup. (Left) Task setup. The primary trajectory-tracking task was performed via a Novint Falcon controller and visual feedback on the screen. The secondary task involved using the "space key" to tap a given rhythm. (Right) Primary task visualisation. The trajectory to be followed was displayed in blue. At each time instant, the red dot showed the position of the center of the end-effector and the green dot showed the target.
• Figure 2: Rhythm Method Visualization (in four-four time). Participants followed the rhythm by tapping the "space" bar. The repeating pattern consisted of a short inter-tap interval followed by a long one (adapted from park2015rhythm).
• Figure 3: Experimental procedure including the training phase and main phase for a given autonomy level $\gamma$.
• Figure 4: (a)-(f) Plots of data distribution of each measure against autonomy. Red bars show the mean values of each distribution. $\ast$ denotes $p<.05$. (g) Correlation between averaged MDMT and trajectory tracking error. (h) Illustration of interaction effect between cognitive load, trust, and robot autonomy.