ALOOF: Autonomous Learning of the Meaning of Objects

ALOOF：自主学习物体的含义

• 批准号: EP/M015777/1
• 负责人: David Parker
• 金额: $ 43.43万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM015777%2F1
• 关键词: ALOOF; Autonomous; Learning; Meaning; Objects

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.

当与人类一起工作并为人类工作时，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物品。尽管在对象实例和类识别方面取得了巨大进步，但机器人始终仅限于了解它经过训练可识别的对象。 ALOOF 的目标是让机器人能够利用网络上的大量知识来了解以前未见过的物体，并在现实世界中行动时使用这些知识。我们将开发技术，让机器人不仅可以使用网络来学习新物体的外观，还可以了解它们的属性，包括它们可能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网络上找到的表示之间进行转换。我们提出的翻译机制是元模态表示（即包含并构造其他模态表示的表示），由元模态实体及其之间的关系组成。单个实体代表单个对象类型，由从机器人传感器或网络提取的模态特征组成。组合的特征链接到与每个实体关联的语义属性。机器人的元模态实体集合被组织成结构化本体，支持形式推理。这种表示方法得到了检测机器人知识差距（即未知物体和属性）以及规划如何填补这些差距的方法的补充。由于机器人新知识的主要来源将是网络，我们还将贡献使用新颖的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。通过将元模态表示与机器人的感知和动作能力联系起来，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，机器人必须在其中寻找物体。我们衡量进展的标准是，在给定特定先验知识的情况下，可以自主解决多少知识差距（即机器人对物体的信息不完整的情况）。我们将把结果整合到多个移动机器人上，包括 MetraLabs SCITOS 机器人和家庭服务机器人 HOBBIT。

项目成果

Towards Lifelong Object Learning by Integrating Situated Robot Perception and Semantic Web Mining

通过集成情境机器人感知和语义网挖掘实现终身对象学习

• 发表时间: 2016
• 作者: Young J

IEEE Robotics and Automated Letters

IEEE 机器人和自动化信件

• 发表时间: 2018
• 期刊: A Survey
• 作者: Kunze L

Semantic Web-Mining and Deep Vision for Lifelong Object Discovery

用于终身对象发现的语义网络挖掘和深度视觉

• 发表时间: 2017
• 作者: Young J

Autonomous Learning of Object Models on a Mobile Robot

移动机器人上对象模型的自主学习

• DOI: 10.1109/lra.2016.2522086
• 发表时间: 2017
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Faulhammer T

Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

从嘈杂的网络数据中学习深度视觉对象模型：如何使其发挥作用

• DOI: 10.48550/arxiv.1702.08513
• 发表时间: 2017
• 期刊: arXiv e-prints
• 作者: Massouh Nizar