Learning the Context in Programming by Demonstration of Manipulation Tasks

通过演示操作任务来学习编程环境

• 批准号: 255319423
• 负责人: Professor Dr.-Ing. Rüdiger Dillmann
• 金额: --
• 依托单位: Humanoids and Intelligence Systems Lab (HIS)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2014
• 资助国家: 德国
• 起止时间: 2013-12-31 至 2017-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/255319423?language=en
• 关键词: Learning; Context; Programming; Demonstration; Manipulation

State-of-the-art service robots are able to learn new manipulation tasks consisting of multiple, defined actions, based on the observation of a human teacher, known as Programming by Demonstration (PbD). The logic structure of the task characterized by branches or alternative actions, can be learned using symbolic PbD-approaches. Subsymbolic PbD-approaches allow to learn atomic actions, which generate executable robot motions.. However, no information about the context, in which the atomic action can be executed, is generated. In order to execute a manipulation task autonomously, an operational description of the context is necessary. The problem to solve is, what are relevant, measurable object properties and how can they be autonomously observed with high reliability by the robot.Visual perception alone is insufficient since the variety of objects in the human environment leads to a large number of ambiguities and false positives. The basis to resolve these issues is a selection of suitable object classifiers, a reduced number of objects to classify and the definition of search regions. Additionally, sensor actions have to be defined to determine object properties, which can't be measured visually. In most state-of-the-art systems, this knowledge is defined manually.Thus, the goal of this project is to extend the PbD-approach to learn the context of a manipulation task based on human observation. In order to achieve this goal, we will integrate and extend methods from scene analysis into the PbD-approach. The second goal is to resolve ambiguities and detect false positives of visual perception algorithms using the learned contexts. We will integrate and extend methods from interactive object detection into the PbD-approach to learn sensor actions efficiently to measure non-visual object properties, e.g. weight, and thereby resolve ambiguities and false detections. Based on the learned context, we can infer the role of unknown objects in the environment, e.g. based on the spatial relation to known objects. The execution of learned manipulation tasks with an object, which was unknown , is without generalization processes not possible. Thus, increasing the generalization capabilities of the robot by interactively adapting learned constraints and goals of a manipulation task to a novel object. The application of morphing methods is planned to transform constraints and goals on the basis of 3D-object-models. With the help of simulation techniques the verification of the transformation and its adjustment is done interactively. The developed algorithms will be implemented on real robot anthropomorphic systems and evaluated using real world examples.

最先进的服务机器人能够根据人类教师的观察来学习由多个定义的动作组成的新操作任务，称为演示编程（PbD）。以分支或替代动作为特征的任务逻辑结构可以使用符号 PbD 方法来学习。子符号 PbD 方法允许学习原子动作，从而生成可执行的机器人运动。但是，不会生成有关可以执行原子动作的上下文的信息。为了自主执行操作任务，需要对上下文进行操作描述。要解决的问题是，什么是相关的、可测量的物体属性，以及机器人如何以高可靠性自主观察它们。由于人类环境中物体的多样性导致大量的模糊性和误报，仅靠视觉感知是不够的。解决这些问题的基础是选择合适的对象分类器、减少分类对象的数量以及搜索区域的定义。此外，必须定义传感器动作来确定无法通过视觉测量的物体属性。在大多数最先进的系统中，这些知识是手动定义的。因此，该项目的目标是扩展 PbD 方法以基于人类观察来学习操作任务的上下文。为了实现这一目标，我们将把场景分析方法集成并扩展到 PbD 方法中。第二个目标是使用学习到的上下文来解决歧义并检测视觉感知算法的误报。我们将把交互式对象检测的方法集成和扩展到 PbD 方法中，以有效地学习传感器动作来测量非视觉对象属性，例如权重，从而解决歧义和错误检测。根据学习到的上下文，我们可以推断环境中未知对象的作用，例如基于与已知物体的空间关系。如果没有泛化过程，使用未知的对象执行学习的操作任务是不可能的。因此，通过交互式地将学习的约束和操作任务的目标适应新物体来提高机器人的泛化能力。变形方法的应用计划在 3D 对象模型的基础上转换约束和目标。借助模拟技术，可以交互地验证转换及其调整。 开发的算法将在真实的机器人拟人系统上实施，并使用现实世界的例子进行评估。

项目成果

Scene recognition for mobile robots by relational object search using Next-Best-View estimates from hierarchical Implicit Shape Models

使用分层隐式形状模型的下一个最佳视图估计，通过关系对象搜索来进行移动机器人的场景识别

• DOI: 10.1109/iros.2016.7759046
• 发表时间: 2016
• 期刊: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Meissner;Schleicher;Hutmacher;Schmidt-Rohr;Dillmann
• 通讯作者: Dillmann

Active scene recognition for programming by demonstration using next-best-view estimates from hierarchical Implicit Shape Models

通过使用分层隐式形状模型中的次最佳视图估计进行演示，进行主动场景识别以进行编程

• DOI: 10.1109/icra.2014.6907680
• 发表时间: 2014
• 期刊: 2014 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Meissner;Reckling;Wittenbeck;Schmidt-Rohr;Dillmann
• 通讯作者: Dillmann

Automated selection of spatial object relations for modeling and recognizing indoor scenes with hierarchical Implicit Shape Models

自动选择空间对象关系，用于使用分层隐式形状模型建模和识别室内场景

• DOI: 10.1109/iros.2015.7353980
• 发表时间: 2015
• 期刊: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Meissner;Hanselmann;Jäkel;Schmidt-Rohr;Dillmann
• 通讯作者: Dillmann