A Multimodal Cross-Attention Framework for Robot Error Detection from Bystander Reactions
This work has demonstrated the effectiveness of this cross-fusion approach for capturing errors and generalising to unseen real-world data, and revealed that smile-related action units and their temporal dynamics serve as primary discriminative cues for error detection.