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

Abstract

Figure 1: Three predictive visualizations for delayed teleoperation. Left: Network visualization shows when commands will take effect. Middle: Path visualization projects the robot's intended trajectory. Right: Envelope visualization extends the path with worst-case deviation bounds indicating possible divergence from environmental disturbances. Abstract 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 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. CHI '26, 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.