ALOOF: Autonomous Learning of the Meaning of Objects

当与人类一起工作或为人类工作时，机器人和自主系统必须了解人类活动中涉及的对象，例如制造中的零部件和工具，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别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所训练识别的对象。Aloof的目标是使机器人能够利用Web上的海量知识来了解以前未见过的对象，并在现实世界中使用这些知识。我们将开发允许机器人使用Web的技术，不仅可以了解新对象的外观，还可以了解它们的属性，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，在机器人在现实世界体验中使用的表示法和在Web上找到的表示法之间进行转换。我们提出的翻译机制是一种元情态表示(即包含和构造来自其他情态的表征的表示)，由元情态实体及其之间的关系组成。单个实体表示单个对象类型，并由从机器人传感器或Web提取的模态特征组成。组合特征被链接到与每个实体相关联的语义属性。机器人的元模型实体集合被组织成支持形式推理的结构化本体。这种表示法与检测机器人知识中的空白(即未知对象和属性)以及规划如何填补这些空白的方法相辅相成。由于机器人的主要新知识来源将是网络，我们还将贡献使用新型机器阅读和计算机视觉算法从网络资源中提取相关知识的技术。通过将元模式表示与机器人的感知和行动能力联系起来，我们将实现网络支持和基于物理的终身学习的创新和强大的组合。我们的场景包括一个开放式的家庭环境，机器人必须在那里找到物体。我们对进展的衡量将是在给定特定的先验知识的情况下，可以自主解决多少知识差距(即机器人关于对象的信息不完整的情况)。我们将在多个移动机器人上集成结果，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.