Performance of Remote Task in Multi‐Robot Construction: Focusing on Pick‐and‐Place

ABSTRACT This study explores the impact of remote human–robot interaction (HRI) configurations on collaborative performance in construction robots. As remote‐based operation of construction robots becomes increasingly common in construction environments, understanding how operator configurations and tool characteristics influence task efficiency is growing in importance. Focusing on the Pick‐and‐Place (P‐A‐P) task—one of the most fundamental and widely used operational patterns—we examined two experimental factors: (1) operator configuration (single operator vs. three‐operator collaboration) and (2) tool type (gripper, coupler press, or concrete vibrator). Experiments were conducted in a controlled construction environment, with the P‐A‐P performance measured by task completion time. Hypothesis 1, that single‐operator tasks outperform collaborative tasks, was tested using Welch's t ‐test; Hypothesis 2, regarding tool effects and their interaction with operator configuration, was examined using two‐way ANOVA. To account for the nested and repeated‐measures structure of the data (trials within robots and robots within operators), linear mixed‐effects models (LMM) were additionally conducted. Results consistently showed that single‐operator tasks were faster, supporting the Ringelmann effect by revealing efficiency losses with increasing team size. Moreover, tool type exerted a significant influence, with complexity increasing task duration, particularly under collaborative conditions. Theoretically, the study advances the IMOI framework for remote multi‐robot collaboration by specifying mediating mechanisms such as coordination overhead and monitoring delay, and by operationalising tool complexity through the Tool Difference Index (TDI). Practically, the findings highlight that designing low‐complexity tools and minimising coordination bottlenecks are critical for enhancing the efficiency of remote construction systems. Overall, this study provides empirical evidence and conceptual guidance for developing more effective multi‐robot strategies in dynamic construction environments.