重要度重み付きデータ検索による少数ショット模倣学習
Data Retrieval with Importance Weights for Few-Shot Imitation Learning
少数の実演データを補強するため、事前データセットから関連サンプルを検索する際に、ターゲットと事前データの分布比（重要度重み）をガウスKDEで推定する手法を提案し、従来の最近傍検索の欠点を改善した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Amber Xie, Rahul Chand, Dorsa Sadigh, Joey Hejna
分類: cs.RO, cs.AI
原文アブストラクト
While large-scale robot datasets have propelled recent progress in imitation learning, learning from smaller task specific datasets remains critical for deployment in new environments and unseen tasks. One such approach to few-shot imitation learning is retrieval-based imitation learning, which extracts relevant samples from large, widely available prior datasets to augment a limited demonstration dataset. To determine the relevant data from prior datasets, retrieval-based approaches most commonly calculate a prior data point's minimum distance to a point in the target dataset in latent space. While retrieval-based methods have shown success using this metric for data selection, we demonstrate its equivalence to the limit of a Gaussian kernel density (KDE) estimate of the target data distribution. This reveals two shortcomings of the retrieval rule used in prior work. First, it relies on high-variance nearest neighbor estimates that are susceptible to noise. Second, it does not account for the distribution of prior data when retrieving data. To address these issues, we introduce Importance Weighted Retrieval (IWR), which estimates importance weights, or the ratio between the target and prior data distributions for retrieval, using Gaussian KDEs. By considering the probability ratio, IWR seeks to mitigate the bias of previous selection rules, and by using reasonable modeling parameters, IWR effectively smooths estimates using all data points. Across both simulation environments and real-world evaluations on the Bridge dataset we find that our method, IWR, consistently improves performance of existing retrieval-based methods, despite only requiring minor modifications.
