标题： 六足步态生成的范围 显示英文标题

标题： SCOPE for Hexapod Gait Generation

摘要： 进化方法之前已被证明是用于六足机器人行走步态的有效学习方法。 然而，随着输入空间变得更加复杂，这些算法快速演化出有效策略的能力会下降。 这种下降是由于解空间的指数级增长，这是由于需要处理更复杂的输入而增加的参数数量导致的。 为了解决这个挑战，我们引入了稀疏余弦优化策略进化（SCOPE）。 SCOPE利用离散余弦变换（DCT）直接从输入矩阵的特征系数中进行学习。 通过截断由DCT返回的系数矩阵，我们可以降低输入的维度，同时保留原始输入的最高能量特征。 我们通过使用SCOPE来学习六足机器人的步态来证明该方法的有效性。 六足控制器获得一个包含先前姿态时间序列信息的矩阵输入，然后通过演化的策略将其转换为步态参数。 在这个任务中，将SCOPE添加到参考算法中使效果提高了20%。 SCOPE通过将时间序列姿态数据的总输入大小从2700减少到54，实现了98%的减少，从而达到了这一结果。 此外，只要每个输出维度不大于相应的输入维度，SCOPE能够将输入压缩到任何输出形状。 本文展示了SCOPE能够显著压缩传送到演化控制器的输入大小，从而在统计学上显著提高了效果。 显示英文摘要

摘要： Evolutionary methods have previously been shown to be an effective learning method for walking gaits on hexapod robots. However, the ability of these algorithms to evolve an effective policy rapidly degrades as the input space becomes more complex. This degradation is due to the exponential growth of the solution space, resulting from an increasing parameter count to handle a more complex input. In order to address this challenge, we introduce Sparse Cosine Optimized Policy Evolution (SCOPE). SCOPE utilizes the Discrete Cosine Transform (DCT) to learn directly from the feature coefficients of an input matrix. By truncating the coefficient matrix returned by the DCT, we can reduce the dimensionality of an input while retaining the highest energy features of the original input. We demonstrate the effectiveness of this method by using SCOPE to learn the gait of a hexapod robot. The hexapod controller is given a matrix input containing time-series information of previous poses, which are then transformed to gait parameters by an evolved policy. In this task, the addition of SCOPE to a reference algorithm achieves a 20% increase in efficacy. SCOPE achieves this result by reducing the total input size of the time-series pose data from 2700 to 54, a 98% decrease. Additionally, SCOPE is capable of compressing an input to any output shape, provided that each output dimension is no greater than the corresponding input dimension. This paper demonstrates that SCOPE is capable of significantly compressing the size of an input to an evolved controller, resulting in a statistically significant gain in efficacy.