通过意图识别实现人机协作的自适应切换，提升多任务场景下机器人响应能力。

In Human-Robot Collaboration (HRC), which encompasses physical interaction and remote cooperation, accurate estimation of human intentions and seamless switching of collaboration modes to adjust robot behavior remain paramount challenges. To address these issues, we propose an Intent-Driven Adaptive Generalized Collaboration (IDAGC) framework that leverages multimodal data and human intent estimation to facilitate adaptive policy learning across multi-tasks in diverse scenarios, thereby facilitating autonomous inference of collaboration modes and dynamic adjustment of robotic actions. This framework overcomes the limitations of existing HRC methods, which are typically restricted to a single collaboration mode and lack the capacity to identify and transition between diverse states. Central to our framework is a predictive model that captures the interdependencies among vision, language, force, and robot state data to accurately recognize human intentions with a Conditional Variational Autoencoder (CVAE) and automatically switch collaboration modes. By employing dedicated encoders for each modality and integrating extracted features through a Transformer decoder, the framework efficiently learns multi-task policies, while force data optimizes compliance control and intent estimation accuracy during physical interactions. Experiments highlights our framework's practical potential to advance the comprehensive development of HRC.