Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects
Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects
复制标题
对未知物体同时进行触觉探索和抓握细化
DOI：
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发表时间：
2021
影响因子：
5.2
通讯作者：
Yasemin Bekiroglu
中科院分区：
文献类型：
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作者：
Cristiana Miranda de Farias;Naresh Marturi;R. Stolkin;Yasemin Bekiroglu
这封信解决了同时探索未知物体以模拟其形状的问题，使用机器人手指上的触觉传感器，同时还改善手指放置以优化抓握稳定性。在许多情况下，机器人将仅具有被观察对象的近侧的部分相机视图，远侧保持被遮挡。我们展示了如何初步把握尝试，根据初步猜测的整体对象的形状，产生触觉扫视的远侧的对象，使形状估计，从而连续的把握得到改善。我们提出了一个把握探索方法，使用概率表示的形状，高斯过程隐式曲面的基础上。这种表示使得初始部分视觉数据能够用来自连续触觉扫视的附加数据来增强。这是结合把握质量的概率估计，以完善把握配置。当选择下一组手指放置时，使用双目标优化方法来相互最大化抓取质量并在连续抓取尝试期间改善形状表示。实验结果表明，该方法产生稳定的把握配置比基线方法更有效，同时也产生改进的形状估计的把握对象。
This letter addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object.
DOI：
10.1109/iros45743.2020.9340783
发表时间：
2020-06
期刊：
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
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作者：
Qingkai Lu;Mark Van der Merwe;Tucker Hermans
通讯作者：
Qingkai Lu;Mark Van der Merwe;Tucker Hermans