In this paper, a new method for offline programming part assembly operations with tight fittings is presented. More specifically, an assembly process trajectory generator with self programming capabilities is developed where the user needs to provide only very sparse and intuitive input. The presented system is added to the existing skill based robot software package VEROSIM. In VEROSIM, the trajectory generator is applied to an industrial test platform for assembling insulin injection devices at the Danish pharmaceutical company Novo Nordisk, where it is shown that the trajectories as expected are executable. Hence, the method is a strong alternative to online approaches such as programming by demonstration.

A general requirement for any service robot is to be flexible and capable of processing uncertainties, thus making it adaptable for multiple tasks. As a result, learning the appropriate action parameters for a specific action is a crucial task. The method presented in this paper is an incremental statistical learning method that takes into consideration the uncertainties and the contact forces to find the optimal parameter sets. The method is inspired by solutions available in industrial robotics and it uses a dynamic simulator and Kernel Density Estimation in order to find the parameter sets that lead to a successful Peg-in-Hole action. The solution obtained in the simulation is successfully tested on a real service robot.