Learning Modes of Within-Hand Manipulation
Learning Modes of Within-Hand Manipulation
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DOI：
10.1109/icra.2018.8461187
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
B. Çalli;K. Srinivasan;A. S. Morgan;A. Dollar
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文献类型：
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作者：
B. Çalli;K. Srinivasan;A. S. Morgan;A. Dollar
在这项工作中，我们研究了在不使用触觉传感器的情况下，基于握持指尖的手内操作中发生的四种现象(模式)的检测方法。通过使用致动器状态和视觉数据，我们的目标是识别不同的操作模式，例如解释手是否要放下物体，物体是否会开始滑动到手指上，或者系统是否处于或接近奇点。为此，我们利用监督学习技术，允许我们在不使用系统的机械模型的情况下检测模式。我们通过执行机构和视觉数据分析了特定特征的单独作用，并确定了对检测操作模式最有意义的特征。我们的结果表明，当组合使用执行器和视觉特征时，分类性能达到96%(使用额外的树、梯度提升或支持向量机)。有趣的是，我们能够在仅使用致动器信息的情况下获得94%的识别率，并且仅使用视觉信息能够达到93%的识别率。总体而言，分类器将执行器位置、执行器负载和指令速度识别为检测模式的最重要特征。这些结果对于在不需要手/物体系统的模型的情况下利用最少量的感觉信息来控制手内操纵运动具有重要意义。
In this work, we investigate methods to detect four phenomena (modes) that occur during prehensile fingertip-based within-hand manipulation without the use of tactile sensors. By using actuator states and visual data, we aim to recognize different modes of operation such as interpreting if the hand is about to drop the object, if the object will begin to slide on the fingers, or if the system is at or near a singularity. For this purpose, we utilize supervised learning techniques, which allow us to detect the modes without the use of a mechanical model of the system. We analyze the individual roles of specific features available through both the actuator and visual data, and identify the ones that have the most significance for detecting the operation modes. Our results show classification performance of 96% (using either Extra Trees, Gradient Boosting, or SVM) when using combined actuator and visual features. Interestingly, we were able to achieve a 94% classification rate using only actuator information, and 93 % using only visual information. Overall, the classifiers identified actuator positions, actuator loads, and commanded velocities as the most important features for detecting a mode. These results have implications for enabling the control of within-hand manipulation movements utilizing a minimal amount of sensory information without a model of the hand/object system.