Cross-Modal Causal Inference Facilitates Home Intelligent Robots: Intent Understanding Bias Calibration and Interaction Failure Avoidance in a Dynamic Home Environment

In dynamic home scenarios, due to the complexity of user behavior, the dynamics of environmental conditions, and the fuzziness of multimodal information, the accuracy of intention judgment by service robots has always been relatively low. The degree of association in traditional models depends on statistical correlation, which leads to inaccurate causal judgments and severe interaction failures. Therefore, this study proposes a multimodal causal reasoning model based on the characteristics of dynamic family scenarios, quantitatively determining the corresponding relationship between the key feature information of dynamic family scenarios and the failure transmission mechanism of intention judgment, and constructing a coupling model of influencing factors and interaction failure. Based on the establishment of a causal reasoning model integrating multimodal information such as speech, vision and touch, the intention is modified by using the structural causal model (SCM) and counterfactual causal reasoning, and a causal confidence decision-making framework is designed to achieve early prediction and hierarchical avoidance of interaction failure. The results show that for the above-mentioned real-life interaction scenarios, the intent recognition accuracy of this framework in dynamic scenarios is 24.3% higher than that of the framework using only Transformer fusion, the recognition error rate is reduced by 62.1%, and the number of invalid interactions is decreased by 67%. Meanwhile, it can still maintain an accuracy of over 78% under sudden interferences (such as pet interference, light changes, etc.). This research provides a theoretical reference for the stable decision-making of service robots in a highly dynamic environment.