Abstract
Artificial intelligence is essential to succeed in challenging activities that involve dynamic environments, such as object manipulation tasks in indoor scenes. Most of the state-of-the-art literature explores robotic grasping methods by focusing exclusively on attributes of the target object. When it comes to human perceptual learning approaches, these physical qualities are not only inferred from the object, but also from the characteristics of the surroundings. This work proposes a method that includes environmental context to reason on an object affordance to then deduce its grasping regions. This affordance is reasoned using a ranked association of visual semantic attributes harvested in a knowledge base graph representation. The framework is assessed using standard learning evaluation metrics and the zero-shot affordance prediction scenario. The resulting grasping areas are compared with unseen labelled data to asses their accuracy matching percentage. The outcome of this evaluation suggest the autonomy capabilities of the proposed method for object interaction applications in indoor environments.
Abstract (translated)
人工智能对于成功完成涉及动态环境的具有挑战性的活动至关重要，例如室内场景中的对象操作任务。大多数最先进的文献通过专门关注目标对象的属性来探索机器人抓取方法。当涉及到人类的感知学习方法时，这些物理特性不仅是从物体中推断出来的，而且是从周围环境的特征中推断出来的。本文提出了一种方法，将环境语境引入到一个对象的可供性推理中，从而推导出它的可抓住区域。利用知识库图形表示中获取的视觉语义属性的排序关联来解释这种提供。该框架使用标准学习评估指标和零镜头提供能力预测场景进行评估。将得到的抓取区域与未发现的标记数据进行比较，以评估它们的准确度匹配百分比。评估结果表明，所提出的方法在室内环境中的对象交互应用具有自主能力。
URL
https://arxiv.org/abs/1905.10610
