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Enactive Robot Vision

机译：主动机器人视觉

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Enactivism claims that sensory-motor activity and embodiment are crucial in perceiving the environment and that machine vision could be a much simpler business if considered in this context. However, computational models of enactive vision are very rare and often rely on handcrafted control systems. In this article, we argue that the apparent complexity of the environment and of the robot brain can be significantly simplified if perception, behavior, and learning are allowed to co-develop on the same timescale. In doing so, robots become sensitive to, and actively exploit, characteristics of the environment that they can tackle within their own computational and physical constraints. We describe the application of this methodology in three sets of experiments: shape discrimination, car driving, and wheeled robot navigation. A further set of experiments, where the visual system can develop the receptive fields by means of unsupervised Hebbian learning, demonstrates that the receptive fields are consistently and significantly affected by the behavior of the system and differ from those predicted by most computational models of the visual cortex. Finally, we show that our robots can also replicate the performance deficiencies observed in experiments of motor deprivation with kittens.
机译：Enactivism声称，感觉运动活动和表现力对于感知环境至关重要，如果在这种情况下考虑，机器视觉可能会变得简单得多。但是，主动视觉的计算模型非常少，通常依赖于手工控制系统。在本文中，我们认为，如果允许在相同的时间尺度上共同发展知觉，行为和学习，则可以显着简化环境和机器人大脑的表面复杂性。这样，机器人就可以敏感并积极地利用自己可以在自己的计算和物理约束下解决的环境特征。我们在三组实验中描述了该方法的应用：形状识别，汽车驾驶和轮式机器人导航。视觉系统可以通过无监督的Hebbian学习来发展接受场的另一组实验表明，接受场受到系统行为的一致且显着的影响，并且与大多数视觉计算模型所预测的不同皮层。最后，我们证明了我们的机器人也可以复制在小猫运动剥夺实验中观察到的性能缺陷。

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