标题:具有未知材料特性的物体平面滑动的概率模型:识别和鲁棒规划

摘要】本文介绍了一种新技术,用于学习未知对象的质量和摩擦分布的概率模型,并使用所学习的模型执行鲁棒的滑动动作。所提出的方法在两个连续的阶段中执行。在探索阶段,用机器人从不同角度戳桌面物体。使用各种假设的质量和摩擦模型,将观察到的对象运动与模拟运动进行比较。然后相对于未知质量和摩擦参数来区分仿真到实际的间隙,并使用分析计算出的梯度来优化这些参数。由于在低数据和准静态运动状态下很难将质量与摩擦系数分开,因此我们的方法保留了一组局部最优的质量和摩擦模型对。基于每对模型的相对准确性,计算模型上的概率分布。在开发阶段,使用概率计划器来选择目标配置和稳定可靠的航路点。所提出的技术是在真实对象上并使用真实操纵器进行评估的。结果表明,该技术不仅可以准确地识别非均匀异质物体的质量和摩擦系数,还可以成功地将未知物体滑到桌子的边缘并从那里拾起,而无需任何人工协助或反馈。

Title: A Probabilistic Model for Planar Sliding of Objects with Unknown Material Properties: Identification and Robust Planning

[abstract]  This paper introduces a new technique for learning probabilistic models of mass and friction distributions of unknown objects, and performing robust sliding actions by using the learned models. The proposed method is executed in two consecutive phases. In the exploration phase, a table-top object is poked by a robot from different angles. The observed motions of the object are compared against simulated motions with various hypothesized mass and friction models. The simulation-to-reality gap is then differentiated with respect to the unknown mass and friction parameters, and the analytically computed gradient is used to optimize those parameters. Since it is difficult to disentangle the mass from the friction coefficients in low-data and quasi-static motion regimes, our approach retains a set of locally optimal pairs of mass and friction models. A probability distribution on the models is computed based on the relative accuracy of each pair of models. In the exploitation phase, a probabilistic planner is used to select a goal configuration and waypoints that are stable with a high confidence. The proposed technique is evaluated on real objects and using a real manipulator. The results show that this technique can not only identify accurately mass and friction coefficients of non-uniform heterogeneous objects, but can also be used to successfully slide an unknown object to the edge of a table and pick it up from there, without any human assistance or feedback.

【作者】Changkyu Song, Abdeslam Boularias

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