Autonomous agents embedded in a physical environment need the ability to recognize objects and their properties from sensory data. Such a perceptual ability is often implemented by supervised machine learning models, which are pre-trained using a set of labelled data. In real-world, open-ended deployments, however, it is unrealistic to assume to have a pre-trained model for all possible environments. Therefore, agents need to dynamically learn/adapt/extend their perceptual abilities online, in an autonomous way, by exploring and interacting with the environment where they operate. This paper describes a way to do so, by exploiting symbolic planning. Specifically, we formalize the problem of automatically training a neural network to recognize object properties as a symbolic planning problem (using PDDL). We use planning techniques to produce a strategy for automating the training dataset creation and the learning process. Finally, we provide an experimental evaluation in both a simulated and a real environment, which shows that the proposed approach is able to successfully learn how to recognize new object properties.

翻译：物理环境中的自主代理器需要能够从感官数据中辨别物体及其属性。这种感知能力通常由监督的机器学习模型实施,这些模型使用一组贴标签的数据经过预先培训。然而,在现实世界中,假设对所有可能的环境都有一个预先培训的模式是不现实的。因此,代理器需要以自主的方式在网上动态学习/适应/扩展其感知能力,与操作环境进行探索和互动。本文描述了这样做的方法,利用象征性的规划。具体地说,我们正式确定了自动培训神经网络的问题,以确认物体属性为象征性的规划问题(使用 PDDL)。我们使用规划技术来制定培训数据集创建和学习过程的自动化战略。最后,我们提供了一个模拟和实际环境的实验性评估,这表明拟议的方法能够成功学习如何识别新的物体属性。