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：机器人不仅在工业场所，而且在家庭环境中也日益普及。机器人有效执行操作任务依赖于接触力下的可变阻抗控制。此外，机器人应具备适应能力以应对动态环境中不同机器人任务表现出的显著变化，这种能力可通过人类演示获得。本文提出了一种结合力感知与运动信息的示教学习框架，以促进可变阻抗控制。所提方法涉及从人类演示中估计完整刚度矩阵，随后将其与感知的力及运动信息结合，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，同时通过使用状态依赖的刚度分布，对新任务条件作出适当响应。此外，本文提出了一种基于新型储能器的可变阻抗控制方法，利用学习到的刚度确保无源性。所提方法通过两个虚拟变刚度系统进行评估。首次评估表明，在手动数据集测试中，刚度估计方法相较于传统方法展现出更优的鲁棒性；第二次评估则证明，基于新型储能器的方法相比传统可变阻抗控制方法更易于实现。