ALOOF: Autonomous Learning of the Meaning of Objects

当与人类合作或为人类工作时，机器人和自主系统必须了解人类活动所涉及的对象，例如制造业中的零件和工具，服务应用中使用的专业物品以及辅助生活中的日常生活对象。虽然在对象实例和类识别方面已经取得了很大的进展，但机器人总是局限于了解它已被训练识别的对象。ALOOF的目标是使机器人能够利用网络上的大量知识，以了解以前看不见的对象，并在真实的世界中使用这些知识。我们将开发技术，让机器人使用网络不仅学习新对象的外观，而且还学习它们的属性，包括它们在机器人环境中的位置。为了实现我们的目标，我们将提供一种机制，用于在机器人在现实世界体验中使用的表示与在网络上找到的表示之间进行翻译。我们提出的翻译机制是一个元模态表示（即一个表示，其中包含和结构表示从其他模态），元模态实体和它们之间的关系组成。单个实体表示单个对象类型，并且由从机器人传感器或Web提取的模态特征组成。组合的特征被链接到与每个实体相关联的语义属性。机器人的元模态实体的集合被组织成一个结构化本体，支持形式化推理。这种表示与用于检测机器人知识中的差距（即未知对象和属性）以及用于规划如何填补这些差距的方法相补充。由于机器人的新知识的主要来源将是网络，我们也将贡献技术，从网络资源中提取相关知识，使用新的机器阅读和计算机视觉算法。通过连接元模态表示与机器人的感知和行动能力，我们将实现一个创新的和强大的组合网络支持和物理接地终身学习。我们的场景包括一个开放式的家庭环境，机器人必须找到物体。我们对进展的衡量将是有多少知识缺口（即机器人对物体的信息不完整的情况）可以在给定特定先验知识的情况下自主解决。我们将在多个移动的机器人上集成结果，包括MetraLabs SCITOS机器人和家庭服务机器人HOBBIT。

When working with and for humans, robots and autonomous systems must know about the objects involved in human activities, e.g. the parts and tools in manufacturing, the professional items used in service applications, and the objects of daily life in assisted living. While great progress has been made in object instance and class recognition, a robot is always limited to knowing about the objects it has been trained to recognize. The goal of ALOOF is to enable robots to exploit the vast amount of knowledge on the Web in order to learn about previously unseen objects and to use this knowledge when acting in the real world. We will develop techniques to allow robots to use the Web to not just learn the appearance of new objects, but also their properties including where they might be found in the robot's environment. To achieve our goal, we will provide a mechanism for translating between the representations robots use in their real-world experience and those found on the Web. Our proposed translation mechanism is a meta-modal representation (i.e. a representation which contains and structures representations from other modalities), composed of meta-modal entities and relations between them. A single entity represents a single object type, and is composed of modal features extracted from robot sensors or the Web. The combined features are linked to the semantic properties associated with each entity. The robot's collection of meta-modal entities is organized into a structured ontology, supporting formal reasoning. This representation is complemented with methods for detecting gaps in the knowledge of the robot (i.e. unknown objects and properties), and for planning how to fill these gaps. As the robot's main source of new knowledge will be the Web, we will also contribute techniques for extracting relevant knowledge from Web resources using novel machine reading and computer vision algorithms.By linking meta-modal representations with the perception and action capabilities of robots, we will achieve an innovative and powerful mix of Web-supported and physically-grounded life-long learning. Our scenario consists of an open-ended domestic setting where robots have to find objects. Our measure of progress will be how many knowledge gaps (i.e. situations where the robot has incomplete information about objects), can be resolved autonomously given specific prior knowledge. We will integrate the results on multiple mobile robots including the MetraLabs SCITOS robot, and the home service robot HOBBIT.