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Learning Invariant Sensorimotor Behaviors: A Developmental Approach to Imitation Mechanisms

机译：学习不变的感觉运动行为：模仿机制的发展途径。

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This paper examines the interest of a developmental approach applied to the design of autonomous robots and the understanding of adaptive behaviors, such as imitation. The proposed model is a neural network architecture that learns and uses associations between vision and arm movements, even if the problem is ill posed (in the case of mapping problems between the visual space and the joints space of the arm). The central part of the model is a visuo-motor map able to represent the arm end point's position in an ego-centered space (constrained by the vision) according to motor information (the proprioception). Sensorimotor behaviors such as tracking, pointing, spontaneous imitating, and sequences learning can then be obtained as the consequence of different internal dynamics computed on neural fields triggered by the visuo-motor map. The readout mechanism also explains how an apparently complex behavior can be generated and controlled from one simple internal dynamics and how at the same time the learning problems can be simplified. While highlighting the generic aspect of our model, we show that our robot can autonomously imitate and learn more complex sequences of gestures after the online learning of the visual and proprioceptive control of its hand extremity. Finally, we defend the idea of a co-development of imitative and Sensorimotor capabilities, allowing the acquisition and the building of increasingly complex behavioral capabilities.
机译：本文探讨了一种用于自主机器人设计的开发方法的兴趣以及对诸如模仿之类的自适应行为的理解。所提出的模型是一种神经网络体系结构，即使问题存在不适感（在视觉空间与手臂关节空间之间存在映射问题的情况下），该神经网络体系结构也可以学习并使用视觉与手臂运动之间的关联。模型的中心部分是可见运动图，可以根据运动信息（本体感受）表示手臂端点在以自我为中心的空间中（受视觉限制）的位置。然后，由于在视觉运动图触发的神经场上计算出的不同内部动力学的结果，因此可以获得诸如跟踪，指向，自发模仿和序列学习等感觉运动行为。读出机制还解释了如何从一种简单的内部动力学中产生和控制表面上复杂的行为，以及如何简化学习问题。在强调模型的通用方面时，我们表明，在在线学习手肢的视觉和本体感觉控制之后，我们的机器人可以自动模仿和学习更复杂的手势序列。最后，我们捍卫模仿能力和感觉运动能力共同发展的想法，允许获取和建立日益复杂的行为能力。

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