Learning the Context in Programming by Demonstration of Manipulation Tasks

最先进的服务机器人能够根据人类老师的观察，学习由多个定义动作组成的新操作任务，称为编程演示（PbD）。任务的逻辑结构以分支或替代动作为特征，可以使用符号pbd方法来学习。亚符号pbd方法允许学习原子动作，从而生成可执行的机器人动作。但是，不会生成有关可以执行原子操作的上下文的信息。为了自主地执行操作任务，上下文的操作描述是必要的。要解决的问题是，什么是相关的、可测量的物体属性，以及如何让机器人以高可靠性自主地观察它们。仅凭视觉感知是不够的，因为人类环境中物体的多样性会导致大量的歧义和误报。解决这些问题的基础是选择合适的对象分类器、减少要分类的对象数量和定义搜索区域。此外，必须定义传感器动作以确定物体属性，这是无法通过视觉测量的。在大多数最先进的系统中，这些知识是手动定义的。因此，本项目的目标是扩展pbd方法，以学习基于人类观察的操作任务的上下文。为了实现这一目标，我们将把场景分析方法整合和扩展到pbd方法中。第二个目标是利用学习到的上下文来解决歧义和检测视觉感知算法的误报。我们将集成并扩展交互式对象检测方法到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，例如重量，从而解决歧义和错误检测。基于学习到的上下文，我们可以推断未知物体在环境中的作用，例如，基于与已知物体的空间关系。如果没有泛化过程，对未知对象执行习得的操作任务是不可能的。因此，通过交互式地适应学习到的约束和操作任务的目标来提高机器人的泛化能力。在三维对象模型的基础上，计划应用变形方法对约束和目标进行转换。在仿真技术的帮助下，进行了转换和调整的交互式验证。开发的算法将在真实的机器人拟人化系统上实施，并使用真实世界的示例进行评估。

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.