While visual imitation learning offers one of the most effective ways of
learning from visual demonstrations, generalizing from them requires either
hundreds of diverse demonstrations, task specific priors, or large,
hard-to-train parametric models. One reason such complexities arise is because
standard visual imitation frameworks try to solve two coupled problems at once:
learning a succinct but good representation from the diverse visual data, while
simultaneously learning to associate the demonstrated actions with such
representations. Such joint learning causes an interdependence between these
two problems, which often results in needing large amounts of demonstrations
for learning. To address this challenge, we instead propose to decouple
representation learning from behavior learning for visual imitation. First, we
learn a visual representation encoder from offline data using standard
supervised and self-supervised learning methods. Once the representations are
trained, we use non-parametric Locally Weighted Regression to predict the
actions. We experimentally show that this simple decoupling improves the
performance of visual imitation models on both offline demonstration datasets
and real-robot door opening compared to prior work in visual imitation. All of
our generated data, code, and robot videos are publicly available at
this https URL

提出了一种解耦表示学习与行为学习的视觉模仿学习方法，使用标准的监督和自监督学习方法来学习视觉表示编码器，然后使用非参数局部加权回归来预测行为。实验结果表明，这种简单的解耦可以提高视觉模仿模型在离线演示数据集和实际机器人开门方面的性能。

表示学习在视觉模仿中的惊人有效性

The Surprising Effectiveness of Representation Learning for Visual  Imitation

The weighted k-nearest neighbors algorithm is one of the most fundamental
non-parametric methods in pattern recognition and machine learning. The
question of setting the optimal number of neighbors as well as the optimal
weights has received much attention throughout the years, nevertheless this
problem seems to have remained unsettled. In this paper we offer a simple
approach to locally weighted regression/classification, where we make the
bias-variance tradeoff explicit. Our formulation enables us to phrase a notion
of optimal weights, and to efficiently find these weights as well as the
optimal number of neighbors efficiently and adaptively, for each data point
whose value we wish to estimate. The applicability of our approach is
demonstrated on several datasets, showing superior performance over standard
locally weighted methods.

本文提出了一种简单的，能够使权重最优化的局部加权回归 / 分类方法，并能够为需要估计值的每个数据点高效地找到权重和最优化的邻居数量，从而在多个数据集上展示了比标准局部加权方法更优异的性能表现。

k*- 最近邻居：从全局到局部

k*-Nearest Neighbors: From Global to Local

For many types of machine learning algorithms, one can compute the
statistically `optimal' way to select training data. In this paper, we review
how optimal data selection techniques have been used with feedforward neural
networks. We then show how the same principles may be used to select data for
two alternative, statistically-based learning architectures: mixtures of
Gaussians and locally weighted regression. While the techniques for neural
networks are computationally expensive and approximate, the techniques for
mixtures of Gaussians and locally weighted regression are both efficient and
accurate. Empirically, we observe that the optimality criterion sharply
decreases the number of training examples the learner needs in order to achieve
good performance.

本文回顾了如何使用最优数据选择技术来优化一些类型的机器学习算法，包括前馈神经网络、高斯混合模型和局部加权回归，并探讨了此方法如何降低训练数据量，提高模型性能。