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Learn to wipe: A case study of structural bootstrapping from sensorimotor experience

机译:学习擦拭:从感觉运动经验中进行结构引导的案例研究

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In this paper, we address the question of generative knowledge construction from sensorimotor experience, which is acquired by exploration. We show how actions and their effects on objects, together with perceptual representations of the objects, are used to build generative models which then can be used in internal simulation to predict the outcome of actions. Specifically, the paper presents an experiential cycle for learning association between object properties (softness and height) and action parameters for the wiping task and building generative models from sensorimotor experience resulting from wiping experiments. Object and action are linked to the observed effect to generate training data for learning a non-parametric continuous model using Support Vector Regression. In subsequent iterations, this model is grounded and used to make predictions on the expected effects for novel objects which can be used to constrain the parameter exploration. The cycle and skills have been implemented on the humanoid platform ARMAR-IIIb. Experiments with set of wiping objects differing in softness and height demonstrate efficient learning and adaptation behavior of action of wiping.
机译:在本文中,我们从感觉运动经验中解决生成知识构建的问题,这是通过探索获得的。我们展示了如何使用动作及其对对象的影响以及对象的感知表示来构建生成模型,然后将该模型用于内部仿真中以预测动作的结果。具体来说,本文提出了一个学习周期,用于学习擦拭任务的对象属性(柔软度和高度)与动作参数之间的关联,并根据擦拭实验产生的感觉运动经验建立生成模型。将对象和动作链接到观察到的效果,以生成训练数据,以使用支持向量回归来学习非参数连续模型。在随后的迭代中,此模型被扎根并用于对可用于约束参数探索的新颖对象的预期效果进行预测。该循环和技能已在类人机器人平台ARMAR-IIIb上实现。用一组柔软度和高度不同的擦拭对象进行的实验证明了有效的学习和擦拭动作的适应行为。

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