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.

本文提出了一种基于学习示范的框架，将力传感和运动信息相结合，估计出完整的刚度矩阵，并使用非参数方法创建模型，从而实现了机器人对人类示范任务的复制，并通过特定状态下的刚度剖面对新任务做出适当响应，并提出了一种新型的基于水箱的可变阻抗控制方法来确保无损耗，在两个虚拟变刚度系统上进行了评估，结果表明，与传统方法相比，该方法具有更高的鲁棒性和易于实现。

从演示中学习带有被动保证的可变阻抗技能