Describing upper body motions based on the Labanotation for learning-from-observation robots
This addresses the challenge of learning-from-observation for humanoid robots, though it is incremental as it builds on existing symbolic representation methods.
The paper tackles the problem of enabling robots to mimic human upper body motions by introducing task models based on Labanotation, which serve as an intermediate symbolic representation to bridge kinematic differences, and demonstrates that three different robots can automatically mimic these motions with satisfactory resemblance.
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.