Research indicates that humans can mistakenly assume that robots and humans have the same field of view, possessing an inaccurate mental model of robots. This misperception may lead to failures during human-robot collaboration tasks where robots might be asked to complete impossible tasks about out-of-view objects. The issue is more severe when robots do not have a chance to scan the scene to update their world model while focusing on assigned tasks. To help align humans’ mental models of robots’ vision capabilities, we propose four field-of-view indicators in augmented reality that reveal the robot’s actual horizontal vision limitations to human collaborators and conducted a human-subjects experiment ( \(\textrm{N}=41\) ) to evaluate them in a collaborative assembly task regarding accuracy, confidence, task efficiency, and workload. These indicators span a spectrum of positions: two at robot’s eye and head space—deepening eye socket and adding blocks to two sides of the eyes (i.e., egocentric), and two anchoring in the robot’s task space—adding extended blocks from the sides of eyes to the table and placing blocks directly on the tables (i.e., allocentric). Results showed that, when placed directly in the task space, the allocentric indicator yields the highest accuracy, although with a delay in interpreting the robot’s field of view. When placed at the robot’s eyes, the egocentric indicator of deeper eye sockets, possible for physical alteration, also increased accuracy. In all indicators, participants’ confidence was high while cognitive load remained low. Finally, we contribute six guidelines for practitioners to apply our augmented reality indicators or physical alterations to align humans’ mental models with robots’ vision capabilities.
