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Generalized recognition of single-ended contact formations

机译:单端接触形式的通用识别

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Contact formations have proven useful for programming robots by demonstration for operations involving contact. These techniques require real time recognition of contact formations. Single ended contact formation (SECF) classifiers using only the force/torque measured at the wrist of the robot have been shown to be quite effective for this purpose. To function properly, however, previous SECF classifiers have required a sizable training set and a constant pose between the force/torque sensor and the manipulated object. Thus, if an object is re-grasped and the pose changes, one expects to have to repeat the creation of the training set. We discuss the impact of sensor-object pose changes have on two successful classifiers. Experimental data shows that they perform poorly when sensor-object pose changes. We discuss a method to regain the performance of both classifiers while minimizing the retraining necessary.
机译:通过演示涉及接触的操作,已证明接触形式对机器人编程很有用。这些技术需要实时识别接触形式。已经证明仅使用在机器人手腕处测得的力/扭矩的单端接触形成(SECF)分类器是非常有效的。为了正常运行,以前的SECF分类器需要相当大的训练集,并且力/扭矩传感器和被操纵物体之间必须保持恒定的姿势。因此,如果重新抓起一个对象并且姿势发生变化,则人们将不得不重复训练集的创建。我们讨论了传感器-对象姿态变化对两个成功分类器的影响。实验数据表明,当传感器对象的姿势发生变化时,它们的性能较差。我们讨论一种在最小化必要的再训练的同时重新获得两个分类器性能的方法。

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