Not All Errors Are Created Equal: Exploring Human Responses to Robot Errors with Varying Severity

Abstract

Robot errors occurring during situated interactions with humans are inevitable and elicit social responses. While prior research has suggested how social signals may indicate errors produced by anthropomorphic robots, most have not explored Programming by Demonstration (PbD) scenarios or non-humanoid robots. Additionally, how human social signals may help characterize error severity, which is important to determine appropriate strategies for error mitigation, has been subjected to limited exploration. We report an exploratory study that investigates how people may react to technical errors with varying severity produced by a non-humanoid robotic arm in a PbD scenario. Our results indicate that more severe robot errors may prompt faster, more intense human responses and that multimodal responses tend to escalate as the error unfolds. This provides initial evidence suggesting temporal modeling of multimodal social signals may enable early detection and classification of robot errors, thereby minimizing unwanted consequences.