Force interaction is inevitable when robots face multiple operation scenarios. How to make the robot competent in force control for generalized operations such as multi-tasks still remains a challenging problem. Aiming at the reproducibility of interaction tasks and the lack of a generalized force control framework for multi-task scenarios, this paper proposes a novel hybrid control framework based on active admittance control with iterative learning parameters-tunning mechanism. The method adopts admittance control as the underlying algorithm to ensure flexibility, and iterative learning as the high-level algorithm to regulate the parameters of the admittance model. The whole algorithm has flexibility and learning ability, which is capable of achieving the goal of excellent versatility. Four representative interactive robot manipulation tasks are chosen to investigate the consistency and generalisability of the proposed method. Experiments are designed to verify the effectiveness of the whole framework, and an average of 98.21% and 91.52% improvement of RMSE is obtained relative to the traditional admittance control as well as the model-free adaptive control, respectively.

翻译：力交互是机器人面对多种操作场景时不可避免的问题。如何使机器人能够胜任如多任务等通用化操作的力控制，仍是一个具有挑战性的难题。针对交互任务的可重复性以及多任务场景下缺乏通用力控制框架的问题，本文提出了一种基于主动导纳控制与迭代学习参数调节机制的新型混合控制框架。该方法采用导纳控制作为基础算法以确保灵活性，并以迭代学习作为高层算法来调节导纳模型的参数。整个算法兼具灵活性与学习能力，能够实现出色的通用性目标。本文选取了四种具有代表性的机器人交互操作任务，以研究所提方法的一致性与泛化能力。通过实验验证了整体框架的有效性，相对于传统导纳控制与无模型自适应控制，均方根误差分别平均提升了98.21%和91.52%。