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We have been developing a paradigm, which we refer to asLearning-from-observation, for a robot to automatically acquire what-to-dothrough observation of human performance. Since a simple mimicking method torepeat exact joint angles does not work due to the kinematic and dynamicdifference between a human and a robot, the method introduces an intermediatesymbolic representation, task models, to conceptually represent what-to-dothrough observation. Then, these task models are mapped appropriate robotmotions depending on each robot hardware. This paper presents task models,designed based on the Labanotation, for upper body movements of humanoidrobots. Given a human motion sequence, we first analyze the motions of theupper body, and extract certain fixed poses at certain key frames. These keyposes are translated into states represented by Labanotation symbols. Then,task models, identified from the state transitions, are mapped to robotmovements on a particular robot hardware. Since the task models based onLabanotation are independent from different robot hardware, we can share thesame observation module; we only need task mapping modules depending ondifferent robot hardware. The system was implemented and demonstrated thatthree different robots can automatically mimic human upper body motions withsatisfactory level of resemblance.

Describing upper body motions based on the Labanotation for learning-from-observation robots