Abstract
We propose a learning framework, named Multi-Coordinate Cost Balancing (MCCB), to address the problem of acquiring point-to-point movement skills from demonstrations. MCCB encodes demonstrations simultaneously in multiple differential coordinates that specify local geometric properties. MCCB generates reproductions by solving a convex optimization problem with a multi-coordinate cost function and linear constraints on the reproductions, such as initial, target, and via points. Further, since the relative importance of each coordinate system in the cost function might be unknown for a given skill, MCCB learns optimal weighting factors that balance the cost function. We demonstrate the effectiveness of MCCB via detailed experiments conducted on one handwriting dataset and three complex skill datasets.
Abstract (translated)
我们提出了一个名为多坐标成本平衡（MCCB）的学习框架，以解决从演示中获得点对点移动技能的问题。MCCB以指定局部几何特性的多个微分坐标同时编码演示。MCCB通过求解凸优化问题来生成复制品，该问题具有多坐标成本函数和对复制品的线性约束，例如初始值、目标值和通过点。此外，由于每个坐标系在成本函数中的相对重要性对于给定的技能可能是未知的，因此MCCB学习平衡成本函数的最佳加权因子。通过对一个笔迹数据集和三个复杂技能数据集的详细实验，验证了MCCB的有效性。
URL
https://arxiv.org/abs/1903.11725
