Autonomous Active Object Learning Through Robot Manipulation

服务机器人有可能成为我们日常生活中无处不在的助手。他们被设想执行各种任务，从清洁我们的公寓到修剪我们的草坪或喂养我们的宠物。为了成功地解决这些任务，机器人必须能够正确地推理和操纵对象，这需要有关相应对象的丰富知识。为了获得这些知识，机器人不仅需要分析物体的几何形状和外观的方法，而且还需要检索它们的类别并推断它们的特征。这包括，例如，物理属性，如材料和重量，或部件和它们的功能，以及其他形式的信息，如对象的品牌或价格。在这个项目中，我们的目标是学习所有的对象信息，通过利用物理对象交互的可能性，并通过分析万维网上可用的信息，在最小的人的监督。此外，我们的目标是从一个通用模型的操作技能转移到特定的形状和特征的新的物体和自主改进thes.For我们的研究，我们将使用国家的最先进的机器人配备了机械手，我们提出了一个新的主动学习的角度。S.我们为机器人提供有关对象的先验知识，并让机器人访问网络上的信息，这些信息不断更新，并且具有很大的多样性。机器人通过与环境的交互测试估计的假设来主动学习，例如，通过获取对象数据、推动或拾取对象。它从演示中学习操纵物体的新技能，并将其推广到功能相似的物体，并从这些物体的经验中进行改进。ALROMA将推进主动和机器人学习，物体操纵和物体发现的最新技术，并将为下一代服务机器人做出贡献。

Service robots have the potential to be ubiquitous helpers of our everyday life. They are envisioned to perform a variety of tasks ranging from cleaning our apartments to mowing our lawn or feeding our pets. For solving such tasks successfully, robots must be able to properly reason about and manipulate objects, which requires rich knowledge about the corresponding objects. To obtain such knowledge, robots need not only means for analyzing the geometry and appearance of objects but also for retrieving their categories and inferring their characteristics. This includes, for example, physical properties such as material and weight, or parts and their functions, as well as other forms of information such as object brand or price.In this project, we aim at learning all the object information with minimal human supervision by leveraging the possibility of physical object interaction and by analyzing the information available in the World Wide Web. Furthermore, we aim at the transfer of manipulation skills from a generic model to the specific shape and characteristics of novel objects and the autonomous improvement of these.For our research, we will use state-of-the-art robots equipped with manipulatorWe propose a novel active learning perspective. s. We provide the robot with prior knowledge about objects and give the robot access to the information available on the web that is continually updated and with large diversity. The robot actively learns by testing the estimated hypotheses through interaction with the environment, e.g., by taking object data, pushing or picking an object. It learns novel skills for manipulating the objects from demonstrations, which it generalizes to functionally similar objects and improves from experience with these.ALROMA will advance the state of the art in active and robot learning, object manipulation and object discovery and will contribute to the next generation of service robots.