  We have been developing a paradigm, which we refer to as
Learning-from-observation, for a robot to automatically acquire what-to-do
through observation of human performance. Since a simple mimicking method to
repeat exact joint angles does not work due to the kinematic and dynamic
difference between a human and a robot, the method introduces an intermediate
symbolic representation, task models, to conceptually represent what-to-do
through observation. Then, these task models are mapped appropriate robot
motions depending on each robot hardware. This paper presents task models,
designed based on the Labanotation, for upper body movements of humanoid
robots. Given a human motion sequence, we first analyze the motions of the
upper body, and extract certain fixed poses at certain key frames. These key
poses are translated into states represented by Labanotation symbols. Then,
task models, identified from the state transitions, are mapped to robot
movements on a particular robot hardware. Since the task models based on
Labanotation are independent from different robot hardware, we can share the
same observation module; we only need task mapping modules depending on
different robot hardware. The system was implemented and demonstrated that
three different robots can automatically mimic human upper body motions with
satisfactory level of resemblance.
