Learning human-robot interactions from human-human demonstrations (with applications in Lego rocket assembly)


This video demonstrates a novel imitation learning approach for learning human-robot interactions from human-human demonstrations. During training, the movements of two human interaction partners are recorded via motion capture. From this, an interaction model is learned that inherently captures important spatial relationships as well as temporal synchrony of body movements between the two interacting partners. The interaction model is based on interaction meshes that were first adopted by the computer graphics community for the offline animation of interacting virtual characters. We developed a variant of interaction meshes that is suitable for real-time human-robot interaction scenarios. During humanrobot collaboration, the learned interaction model allows for adequate spatio-temporal adaptation of the robots behavior to the movements of the human cooperation partner. Thus, the presented approach is well suited for collaborative tasks requiring continuous body movement coordination of a human and a robot. The feasibility of the approach is demonstrated with the example of a cooperative Lego rocket assembly task.

In International Conference on Humanoid Robots, IEEE-RAS.

More detail can easily be written here using Markdown and $\rm \LaTeX$ math code.