DCAF: Dynamic Cross-Attention Feature Fusion from Robotic Anomaly Detection to Position Accuracy Modeling

Abstract

In robotic operations, heterogeneous computation tasks and sensor configurations pose significant challenges to analyze different modalities of data for data sharing and collaborative learning in robotic Artificial Intelligence (AI) tasks. The lack of historical data in new scenarios or new computation tasks complicates model training and limits the applicability of existing AI methodologies. Current transfer learning approaches heavily rely on static feature extraction, which fail to dynamically adjust to specific feature relationships between different samples or modalities. In the literature, these methods struggle to capture inter-modal associations effectively, resulting in insufficient information sharing and poor modeling performance. Motivated by these challenges, this paper proposes a Dynamic Cross-Attention Feature Fusion (DCAF) approach to map the features from one robotic AI task to another. By calculating attention weights tailored to each target domain sample, DCAF extracts the most relevant source domain features and generates dynamic fused representations. The proposed approach enables sample-specific feature selection and fine-grained domain alignment, effectively enhancing the modeling performance compared with traditional transfer learning and model training based on the local data source. It is particularly suited for a new robotic AI training task with limited sample size and new data modalities. Experimental results for feature fusion from a robotics anomaly detection dataset to a position accuracy modeling data set demonstrate the effectiveness of DCAF, providing an efficient solution for domain adaptation and multimodal fusion.