Static Is Not Enough: A Comparative Study of VR and SpaceMouse in Static and Dynamic Teleoperation Tasks

TL;DR

This study compares VR controllers and SpaceMouse interfaces in static and dynamic teleoperation tasks, revealing VR's superior performance and usability, especially in dynamic scenarios, and provides an open-source VR teleoperation system.

Contribution

It offers a comprehensive comparison of VR and SpaceMouse interfaces across static and dynamic tasks, highlighting VR's advantages and releasing an open-source VR teleoperation system.

Findings

VR outperforms SpaceMouse in success rate and task duration.

VR has lower workload and higher usability scores.

VR shows better performance in dynamic teleoperation tasks.

Abstract

Imitation learning relies on high-quality demonstrations, and teleoperation is a primary way to collect them, making teleoperation interface choice crucial for the data. Prior work mainly focused on static tasks, i.e., discrete, segmented motions, yet demonstrations also include dynamic tasks requiring reactive control. As dynamic tasks impose fundamentally different interface demands, insights from static-task evaluations cannot generalize. To address this gap, we conduct a within-subjects study comparing a VR controller and a SpaceMouse across two static and two dynamic tasks ($N=25$). We assess success rate, task duration, cumulative success, alongside NASA-TLX, SUS, and open-ended feedback. Results show statistically significant advantages for VR: higher success rates, particularly on dynamic tasks, shorter successful execution times across tasks, and earlier successes across attempts, with significantly lower workload and higher usability. As existing VR teleoperation systems are rarely open-source or suited for dynamic tasks, we release our VR interface to fill this gap.