一种用于快速泛化的非参数化技能表示方法
A Non-parametric Skill Representation with Soft Null Space Projectors for Fast Generalization
摘要：
在过去的二十年里，机器人社区见证了各种运动表示的出现，这些运动表示已被广泛使用，特别是在人工克隆中，以压缩编码和概括技能。其中，概率方法赢得了相关的地方，由于其编码的变化，相关性和适应性，以新的任务条件。然而，调制这样的基元通常是麻烦的，因为需要参数重新优化，这经常需要计算上昂贵的操作。在本文中，我们推导出一个非参数的运动原语配方，其中包含一个零空间投影仪。我们表明，这样的配方允许快速和有效的运动生成与计算复杂性O（n2），而不涉及矩阵求逆，其复杂性为O（n3）。这是通过使用零空间来跟踪次级目标来实现的，精度由训练数据集确定。使用与时间输入相关联的2D例子，我们表明，我们的非参数解决方案相比，有利的国家的最先进的参数化方法。对于具有高维输入的演示技能，我们表明它也允许动态适应。
英文摘要：
Over the last two decades, the robotics community witnessed the emergence of various motion representations that have been used extensively, particularly in behavorial cloning, to compactly encode and generalize skills. Among these, probabilistic approaches have earned a relevant place, owing to their encoding of variations, correlations and adaptability to new task conditions. Modulating such primitives, however, is often cumbersome due to the need for parameter re-optimization which frequently entails computationally costly operations. In this paper we derive a non-parametric movement primitive formulation that contains a null space projector. We show that such formulation allows for fast and efficient motion generation with computational complexity O(n2) without involving matrix inversions, whose complexity is O(n3). This is achieved by using the null space to track secondary targets, with a precision determined by the training dataset. Using a 2D example associated with time input we show that our non-parametric solution compares favourably with a state-of-the-art parametric approach. For demonstrated skills with high-dimensional inputs we show that it permits on-the-fly adaptation as well.
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