Every Move You Make: Visualizing Near-Future Motion Under Delay for Telerobotics

要旨

Delays in direct teleoperation decouple operator input from robot feedback. We frame this not as a unitary problem but as three facets of operator uncertainty: (1) communication, when commands take effect, (2) trajectory, how inputs map to motion, and (3) environmental, how external factors alter outcomes. We externalized each facet through predictive visualizations: Network, Path, and Envelope. In a controlled study with 24 participants (novices in telerobotics) navigating a simulated robot under a fixed 2.56 s round-trip delay, we compared these visualizations against a delayed-video baseline. Path significantly shortened task time, lowered perceived cognitive load, and reduced reliance on reactive "move-and-wait" behavior. Envelope lowered cognitive load but did not significantly reduce reactive behavior or improve performance, while Network had no measurable effect. These results indicate that predictive support is effective only when trajectory uncertainty is externalized, enabling operators to move from reactive to more proactive control.

著者
Dries Cardinaels
UHasselt - Flanders Make, Diepenbeek, Belgium
Raf Ramakers
UHasselt - Flanders Make, Diepenbeek, Belgium
Tom Veuskens
UHasselt - Flanders Make, Diepenbeek, Belgium
Thomas Pietrzak
Univ. Lille, CNRS, Inria, Centrale Lille, UMR 9189 CRIStAL, Lille, France
Gustavo Alberto. Rovelo Ruiz
UHasselt - Flanders Make, Diepenbeek, Belgium
Kris Luyten
UHasselt - Flanders Make, Diepenbeek, Belgium
動画

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Human-Robot Interaction & Embodied Sensing

P1 - Room 134
7 件の発表
2026-04-15 18:00:00
2026-04-15 19:30:00