Robots are increasingly being deployed not only in workplaces but also in households. Effectively execute of manipulation tasks by robots relies on variable impedance control with contact forces. Furthermore, robots should possess adaptive capabilities to handle the considerable variations exhibited by different robotic tasks in dynamic environments, which can be obtained through human demonstrations. This paper presents a learning-from-demonstration framework that integrates force sensing and motion information to facilitate variable impedance control. The proposed approach involves the estimation of full stiffness matrices from human demonstrations, which are then combined with sensed forces and motion information to create a model using the non-parametric method. This model allows the robot to replicate the demonstrated task while also responding appropriately to new task conditions through the use of the state-dependent stiffness profile. Additionally, a novel tank based variable impedance control approach is proposed to ensure passivity by using the learned stiffness. The proposed approach was evaluated using two virtual variable stiffness systems. The first evaluation demonstrates that the stiffness estimated approach exhibits superior robustness compared to traditional methods when tested on manual datasets, and the second evaluation illustrates that the novel tank based approach is more easily implementable compared to traditional variable impedance control approaches.

翻译：机器人正越来越多地在工作场所及家庭中得到部署。机器人有效执行操作任务依赖于接触力作用下的变阻抗控制。此外，机器人应具备自适应能力以应对动态环境中不同机器人任务呈现的巨大变化，这种能力可通过人类演示获得。本文提出了一种结合力觉传感与运动信息的示教学习框架，用于实现变阻抗控制。该方法包含从人类演示中估计完整刚度矩阵的步骤，随后将估计结果与传感力及运动信息结合，利用非参数方法构建模型。该模型使机器人能够复现演示任务，同时通过利用状态依赖的刚度分布特性，对新的任务条件做出适当响应。此外，本文提出一种基于新型容器的变阻抗控制方法，通过使用学习到的刚度来确保无源性。该方法在两种虚拟变刚度系统中进行了评估。第一项评估表明，在人工数据集上测试时，所提刚度估计方法相比传统方法具有更优的鲁棒性；第二项评估显示，与传统变阻抗控制方法相比，该新型容器方法更易于实现。