Analytic grasp success prediction with tactile feedback
Analytic grasp success prediction with tactile feedback
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DOI：
10.1109/icra.2016.7487130
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发表时间：
2016-05
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影响因子：
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通讯作者：
R. Krug;A. Lilienthal;D. Kragic;Yasemin Bekiroglu
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文献类型：
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作者：
R. Krug;A. Lilienthal;D. Kragic;Yasemin Bekiroglu
在许多机器人应用中，预测抓取成功对于避免失败是有用的。基于扳手空间的推理，我们解决了如果将触觉反馈纳入分析抓取成功预测工作的问题。触觉信息可以减轻接触位置的不确定性，方便接触建模。我们引入了一个基于扳手的分类器，并在一个大的真实抓取集上对其进行了评估。这项工作的关键发现是，利用触觉信息可以使基于扳手的推理与基于学习或模拟的现有方法在同一水平上执行。与这些方法不同的是，该方法不需要训练数据，建模工作量小，计算效率高。此外，我们的方法通过以物理上有意义的方式考虑抓取装置的能力和预期的扰动力/力矩，从而提供任务泛化。
Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information can alleviate contact placement uncertainties and facilitates contact modeling. We introduce a wrench-based classifier and evaluate it on a large set of real grasps. The key finding of this work is that exploiting tactile information allows wrench-based reasoning to perform on a level with existing methods based on learning or simulation. Different from these methods, the suggested approach has no need for training data, requires little modeling effort and is computationally efficient. Furthermore, our method affords task generalization by considering the capabilities of the grasping device and expected disturbance forces/moments in a physically meaningful way.