Leg Task Models for Reproducing Human Dance Motions on Biped Humanoid Robots

Abstract

In this paper, we propose a method that enables a biped humanoid robot to reproduce human dance motions with its whole body. Our method is based on the paradigm ofLearning from Observation. In this study, a robot uses its own legs to support the body during a dance performance. We proposeleg task models, which can solve the problems caused by severe constraints in adapting human motions to the legs of a robot. First, elements of the leg task models are recognized from motion data captured from human performances. Then motion data of a robot is regenerated from the recognized elements so that the motion is stably executable on the robot. Our method was verified by experiments on a humanoid robotHRP-2using a traditional folk dance. HRP-2 successfully performed dance motions that were automatically reproduced from motion data captured from human dance performances.