Exploring Human-Robot Collaboration: Analysis of Interaction Modalities in Challenging Tasks

2026-05-13

AI Summary

This study investigates how to design human-robot interaction modalities in memory-dependent block-building tasks to enhance user experience. By developing and comparing three interaction modes—passive, reactive, and proactive—the work presents the first systematic evaluation of how proactive robotic assistance, offered without explicit user requests, influences user preference and perceived utility. User experiments conducted on a mobile robot platform reveal that, although proactive support slightly increases task completion time, 67% of participants preferred this modality and 78% rated it as the most useful. These findings demonstrate that well-timed proactive behaviors can significantly improve collaborative experiences, offering empirical grounding for the design of intelligent collaborative systems.

Abstract

This work compares three interaction modalities for human-robot collaboration: passive, reactive, and proactive. We studied 18 participants assembling a seven-layer colored tower from memory while using nearby and distant blocks. In the passive modality participants worked alone; in the reactive modality a mobile robot helped only upon request; in the proactive modality it initiated brick delivery and error signaling without explicit requests. Although robot assistance increased completion time, most participants preferred collaboration: 67% preferred proactive behavior and 78% judged it most useful. These results suggest that timely proactive support can improve user experience in controlled collaborative tasks.

Problem

• human-robot collaboration
• interaction modalities
• proactive behavior
• user experience
• challenging tasks

Innovation

• human-robot collaboration
• interaction modalities
• proactive assistance
• user preference
• collaborative tasks