Beyond Coulomb: Stochastic Friction Models for Practical Grasping and Manipulation
Beyond Coulomb: Stochastic Friction Models for Practical Grasping and Manipulation
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DOI：
10.1109/lra.2023.3292580
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发表时间：
2023-08
影响因子：
5.2
通讯作者：
Zixi Liu;R. Howe
中科院分区：
文献类型：
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作者：
Zixi Liu;R. Howe
在日常任务和非结构化环境中可靠的抓取和操纵需要准确的接触建模和抓取稳定性估计。一个关键的组成部分是摩擦系数，它是可变的，取决于许多因素。然而，机器人应用程序经常使用库仑的摩擦模型，该模型忽略了这种可变性，转而假设摩擦系数是一个常数。在本工作中，我们进行了机器人手指和机械手的滑动实验，结果表明橡胶摩擦力随法向力F_(n})和接触速度(V)的变化而强烈变化，并且包含一个显著的随机分量。我们给出了一个将摩擦系数建模为分布而不是常量的框架，并说明了当给定$Fn}$或$v$的先验信息时，这种分布是如何缩小的。对于给定的分布，滑动的可能性是关于切向力与法向力比的连续函数，而不是根据库仑定律的阶跃函数。通过将摩擦力建模为$Fn}$和$v$的函数，我们证明了通过指尖对物体表面的单个滑动笔划可以使用回归模型来估计摩擦力参数，并且跨越$Fn}$-$v$空间的更大范围的笔划提供了更好的摩擦力估计。这些结果可以应用于抓取控制，以实现滑动可能性与抓持力水平之间的定量权衡，并通过阐明期望速度和预期力量水平之间的关系来进行滑动操作规划。将该模型应用到机器学习中，通过提供更准确的摩擦行为表示，有可能增强强化学习和从模拟到真实的迁移。
Reliable grasping and manipulation in daily tasks and unstructured environments require accurate contact modeling and grasp stability estimation. A key component is the coefficient of friction, which is variable and dependent on many factors. However, robotics applications often use Coulomb's model of friction, which ignores this variability and instead assumes that the coefficient of friction is a constant. In this work, we conducted sliding experiments with robot fingers and a robot hand, and show that rubber friction varies strongly with normal force $F_{n}$ and contact velocity $v$, and includes a significant stochastic component. We present a framework for modeling the coefficient of friction $\mu$ as a distribution rather than a constant, and show how this distribution can be narrowed when given a prior on $F_{n}$ or $v$. For a given distribution, the likelihood of slipping is a continuous function with respect to the tangential-to-normal force ratio, instead of a step function according to Coulomb's law. By modeling friction as a function of $F_{n}$ and $v$, we demonstrate that friction parameters can be estimated using regression models from a single sliding stroke of the fingertip against the object surface, and that strokes spanning a larger range of $F_{n}$-$v$ space provide better friction estimates. These results can be applied to grasp control to enable a quantitative trade-off between the likelihood of slipping vs. grasp force levels, and to sliding manipulation planning by elucidating the relationship between desired velocity and anticipated force levels. Application of this model to machine learning has the potential to enhance reinforcement learning and sim-to-real transfer by providing more accurate representations of frictional behavior.