Robots that physically interact with their surroundings, in order to accomplish some tasks or assist humans in their activities, require to exploit contact forces in a safe and proficient manner. Impedance control is considered as a prominent approach in robotics to avoid large impact forces while operating in unstructured environments. In such environments, the conditions under which the interaction occurs may significantly vary during the task execution. This demands robots to be endowed with on-line adaptation capabilities to cope with sudden and unexpected changes in the environment. In this context, variable impedance control arises as a powerful tool to modulate the robot's behavior in response to variations in its surroundings. In this survey, we present the state-of-the-art of approaches devoted to variable impedance control from control and learning perspectives (separately and jointly). Moreover, we propose a new taxonomy for mechanical impedance based on variability, learning, and control. The objective of this survey is to put together the concepts and efforts that have been done so far in this field, and to describe advantages and disadvantages of each approach. The survey concludes with open issues in the field and an envisioned framework that may potentially solve them.

翻译：与周围环境发生物理互动的机器人,为了完成某些任务或协助人类开展活动,需要以安全和熟练的方式利用接触力量。阻碍控制被认为是机器人中的一种突出的方法,以避免在结构化环境中操作的大规模冲击力。在这种环境中,相互作用的条件在任务执行期间可能大不相同。这要求机器人具备在线适应能力,以应对环境的突然和意外变化。在这方面,阻碍控制变异,成为调整机器人行为以适应周围变化的强大工具。在这次调查中,我们从控制和学习的角度(单独和联合)介绍专为阻碍控制变异而采用的最新方法。此外,我们建议根据变异、学习和控制,对机械阻碍进行新的分类。这次调查的目的是汇集迄今为止在这方面已经做的概念和努力,并描述每一种方法的优劣之处。调查以实地的开放问题和可能解决这些问题的构想框架结束。