论文标题
具有软空空间投影仪的非参数技能表示，用于快速概括
A Non-parametric Skill Representation with Soft Null Space Projectors for Fast Generalization
论文作者
论文摘要
在过去的二十年中，机器人社区见证了各种运动表示的出现，这些表现已被广泛使用，尤其是在克隆中，以紧凑和概括技能。其中，由于它们编码了变化，相关性和对新任务条件的适应性，因此概率方法获得了相关位置。但是，由于需要重新优化的参数重视经常需要计算上昂贵的操作，因此调节此类原语通常很麻烦。在本文中，我们得出了一个非参数运动原始配方，其中包含空空间投影仪。我们表明，这种公式允许使用计算复杂性O（N2）快速有效地产生运动，而无需矩阵反转，其复杂性为O（n3）。这是通过使用零空间来跟踪辅助目标的精度来实现的。使用与时间输入相关的2D示例，我们表明我们的非参数解决方案与最先进的参数方法相比有利。对于具有高维投入的演示技能，我们表明它也允许在线适应。
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.
