FROM PERCEPTION-ACTION LOOPS TO IMITATION PROCESSES: A BOTTOM-UP APPROACH OF LEARNING BY IMITATION
- 1 October 1998
- journal article
- research article
- Published by Taylor & Francis in Applied Artificial Intelligence
- Vol. 12 (7-8), 701-727
- https://doi.org/10.1080/088395198117596
Abstract
This paper proposes a neural architecture for a robot in order to learn how to imitate a sequence of movements performed by another robot or by a human. The main idea is that the imitation process does not need to be given to the system but can emerge from a misinterpretation of the perceived situation at the level of a simple sensory-motor system. The robot controller is based on a Perception-Action (PerAc) architecture. This architecture allows an autonomous robot to learn by itself sensory-motor associations with a delayed reward. Here, we show how the same architecture can also be used by a “student” robot to learn to imitate another robot, allowing the student robot to discover by itself solutions to a particular problem or to learn from another robot what to do. We discuss the difficulty linked to the segmentation of the actions to imitate. This imitation problem is demonstrated by a task of learning a sequence of movements and their precise timing. Another interesting aspect of this work is that the neural network (NN) used for sequence learning is directly inspired from a brain structure named the hippocampus and mainly involved in memory processes (Banquet et al., 1997). We discuss the importance of imitation processes for the understanding of our high-level cognitive abilities linked to self-recognition and to the recognition of the other as something similar to me.Keywords
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