A Probabilistic Framework for Learning Kinematic Models of Articulated Objects
A Probabilistic Framework for Learning Kinematic Models of Articulated Objects
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DOI：
10.1613/jair.3229
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发表时间：
2011-01-01
影响因子：
5
通讯作者：
Burgard, Wolfram
中科院分区：
文献类型：
--
作者：
Sturm, Juergen;Stachniss, Cyrill;Burgard, Wolfram
在国内环境中运行的机器人通常需要与铰接式物体（例如门，橱柜，洗碗机或冰箱）进行交互。在这项工作中，我们提出了一个新颖的，概率的框架，用于将表达的对象建模为运动图。该图中的顶点对应于对象部分，而它们之间的边缘对其运动关系进行建模。特别是，我们提出了一组参数和非参数边缘模型，以及如何从嘈杂的姿势观察中估算它们。我们还描述了如何估计运动学结构以及如何使用学到的运动学模型进行姿势预测和机器人操纵任务。我们最终介绍了如何将学习模型推广到新的和以前看不见的对象。在使用带有不同摄像头系统的真实机器人以及模拟的各种实验中，我们表明我们的方法是有效，准确和高效的。此外，我们证明我们的方法具有广泛的应用程序，尤其是用于移动操作和服务机器人技术的新兴领域。
Robots operating in domestic environments generally need to interact with articulated objects, such as doors, cabinets, dishwashers or fridges. In this work, we present a novel, probabilistic framework for modeling articulated objects as kinematic graphs. Vertices in this graph correspond to object parts, while edges between them model their kinematic relationship. In particular, we present a set of parametric and non-parametric edge models and how they can robustly be estimated from noisy pose observations. We furthermore describe how to estimate the kinematic structure and how to use the learned kinematic models for pose prediction and for robotic manipulation tasks. We finally present how the learned models can be generalized to new and previously unseen objects. In various experiments using real robots with different camera systems as well as in simulation, we show that our approach is valid, accurate and efficient. Further, we demonstrate that our approach has a broad set of applications, in particular for the emerging fields of mobile manipulation and service robotics.