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From visuo-motor self learning to early imitation-a neural architecture for humanoid learning | IEEE Conference Publication | IEEE Xplore

From visuo-motor self learning to early imitation-a neural architecture for humanoid learning


Abstract:

Behavior imitation ability will be a key technology for future human friendly robots. In order to understand the principles and mechanisms of imitation, we take a synthet...Show More

Abstract:

Behavior imitation ability will be a key technology for future human friendly robots. In order to understand the principles and mechanisms of imitation, we take a synthetic cognitive developmental approach, starting with minimum components and create a system that can learn to imitate others. We developed a visuo-motor neural learning system which consists of orientation selective visual movement representation, distributed arm movement representation, and a high-dimensional temporal sequence learning mechanism. The vision and the movement representations model the findings in primate brain, i.e. macaque area MT(or human area V5) and the primary motor area. The learning mechanism is inspired by the finding that there are excessive connections in neonate brain. As our robot explores the visuo-motor self movement patterns, it learns coherent patterns as high-dimensional trajectory attractors. After the learning, a human comes in front of the robot showing arm movements which are similar to the ones in self learning. Although the robot has never seen or programmed to interpret human arm movement, and the detail of visual stimuli are very different, the robot identifies some of the patterns as similar to those in self learning, and responded by generating the previously learned arm movement. In other words, the robot exhibits early imitation ability based on self exploratory learning.
Date of Conference: 14-19 September 2003
Date Added to IEEE Xplore: 10 November 2003
Print ISBN:0-7803-7736-2
Print ISSN: 1050-4729
Conference Location: Taipei, Taiwan

References

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