This paper presents an approach to ensure conditions on Variable Impedance Controllers through the off-line tuning of the parameters involved in its description. In particular, we prove its application to term modulations defined by a Learning from Demonstration technique. This is performed through the assessment of conditions regarding safety and performance, which encompass heuristics and constraints in the form of Linear Matrix Inequalities. Latter ones allow to define a convex optimisation problem to analyse their fulfilment, and require a polytopic description of the VIC, in this case, obtained from its formulation as a discrete-time Linear Parameter Varying system. With respect to the current state-of-art, this approach only limits the term definition obtained by the Learning from Demonstration technique to be continuous and function of exogenous signals, i.e. external variables to the robot. Therefore, using a solution-search method, the most suitable set of parameters according to assessment criteria can be obtained. Using a 7-DoF Kinova Gen3 manipulator, validation and comparison against solutions with relaxed conditions are performed. The method is applied to generate Variable Impedance Controllers for a pulley belt looping task, inspired by the Assembly Challenge for Industrial Robotics in World Robot Summit 2018, to reduce the exerted force with respect to a standard (constant) Impedance Controller. Additionally, method agility is evaluated on the generation of controllers for one-off modifications of the nominal belt looping task setup without new demonstrations.

翻译：本文通过脱线调整其描述所涉参数,提出了确保可变障碍主计长的条件的一种方法,具体地说,我们证明它适用于“从演示技术中学习”所定义的用词调制,这是通过评估安全和性能条件来进行的,其中包括线性矩阵不平等形式的超常性和制约,包括线性矩阵不平等形式的超常性和制约。通过运用这些方法,可以确定一个相近的优化问题,以分析其履行情况,并要求对维也纳国际中心进行多专题描述,在此情况下,从作为独立时间线性线性分辨分辨分辨系统对维也纳国际中心进行多专题描述。关于目前的状况,这一方法仅限制“从演示技术中学习”所获术语定义的持续和外源性信号的功能,即机器人的外部变量。因此,利用一种解决方案研究方法,可以根据评估标准确定最合适的一套参数。使用一种7-DoF Nenova Gen3调控、验证和比较方法,在采用宽松条件的解决方案的情况下,对维也纳国际中心进行多功能性不固定性控制员,用以在不固定性系统上产生固定的固定性控制器,在不固定性上,在不使用大会标准上,对最高机构性统定值上,在采用一个标准上调,对最高机构性统定值上,以降低机构性调整。