标题： IDAGC：通过人类意图估计和多模态策略学习的自适应通用人机协作 显示英文标题

标题： IDAGC: Adaptive Generalized Human-Robot Collaboration via Human Intent Estimation and Multimodal Policy Learning

摘要： 在人机协作（HRC）中，涵盖物理交互和远程协作，准确估计人类意图以及无缝切换协作模式以调整机器人行为仍然是主要挑战。 为解决这些问题，我们提出了一种基于意图的自适应通用协作（IDAGC）框架，该框架利用多模态数据和人类意图估计来促进在不同场景下的多任务自适应策略学习，从而实现协作模式的自主推理和机器人动作的动态调整。 该框架克服了现有HRC方法的局限性，这些方法通常仅限于单一协作模式，并缺乏识别和在多种状态之间过渡的能力。 我们框架的核心是一个预测模型，它通过条件变分自编码器（CVAE）捕捉视觉、语言、力和机器人状态数据之间的相互依赖关系，以准确识别人类意图并自动切换协作模式。 通过为每种模态使用专用编码器，并通过Transformer解码器集成提取的特征，该框架高效地学习多任务策略，同时力数据在物理交互过程中优化合规控制和意图估计的准确性。 实验表明了我们框架在推动HRC全面发展的实际潜力。 显示英文摘要

摘要： 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.