Imitating human reaching motions using physically inspired optimization principles

Abstract

We present an end-to-end framework which equips robots with the capability to perform reaching motions in a natural human-like fashion. A markerless, high-accuracy, model- based human motion tracker is used to observe how humans perform everyday activities in real-world scenarios. The obtained trajectories are clustered to represent different types of manipulation and reaching motions occurring in a kitchen environment. Using bilevel optimization methods a combination of physically inspired optimization principles is determined that describes the human motions best. For humanoid robots like the iCub these principles are used to compute reaching motion trajectories which are similar to human behavior and respect the individual requirements of the robotic hardware.