  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.
