Towards Understanding Articulated Objects
Towards Understanding Articulated Objects
复制标题
理解铰接物体
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Wolfram Burgard
中科院分区：
文献类型：
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作者：
Jürgen Sturm;C. Stachniss;V. Pradeep;Christian Plagemann;K. Konolige;Wolfram Burgard
在家庭环境中操作的机器人必须能够与铰接物体（如门或抽屉）进行交互。理想情况下，机器人能够通过观察自主推断关节模型。在本文中，我们提出了一种方法来学习运动学模型，通过推断连接的刚性部件和相应的链接的关节模型。我们的方法使用参数化和无参数表示的混合。为了获得无参数模型，我们寻求潜在的行动变量的低维流形，以提供最好的解释给定的观察。通过高斯过程回归学习从铰接杆的约束流形到工作空间的映射。我们的方法已经实施和评估使用真实的数据在各种家庭环境设置。最后，我们讨论了所提出的方法的局限性和可能的扩展。
Robots operating in home environments must be able to interact with articulated objects such as doors or drawers. Ideally, robots are able to autonomously infer articulation models by observation. In this paper, we present an approach to learn kinematic models by inferring the connectivity of rigid parts and the articulation models for the corresponding links. Our method uses a mixture of parameterized and parameter-free representations. To obtain parameter-free models, we seek for low-dimensional manifolds of latent action variables in order to provide the best explanation of the given observations. The mapping from the constrained manifold of an articulated link to the work space is learned by means of Gaussian process regression. Our approach has been implemented and evaluated using real data obtained in various home environment settings. Finally, we discuss the limitations and possible extensions of the proposed method.