Abstract

To engage in cooperative activities with human
partners, robots have to possess basic interactive abilities
and skills. However, programming such interactive skills is a
challenging task, as each interaction partner can have different
timing or an alternative way of executing movements. In this
paper, we propose to learn interaction skills by observing how
two humans engage in a similar task. To this end, we introduce
a new representation called Interaction Primitives. Interaction
primitives build on the framework of dynamic motor primitives
(DMPs) by maintaining a distribution over the parameters of
the DMP. With this distribution, we can learn the inherent
correlations of cooperative activities which allow us to infer the
behavior of the partner and to participate in the cooperation.
We will provide algorithms for synchronizing and adapting the
behavior of humans and robots during joint physical activities.