COLLAGE: 適応的融合に基づく検索による拡張方策学習
COLLAGE: Adaptive Fusion-based Retrieval for Augmented Policy Learning
少数の模倣学習デモから大規模データセットを検索する際、複数の特徴量に基づく適応的な後期融合で関連デモを選択し、重要度サンプリングで方策学習を改善する手法を提案。
詳しい要約
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著者: Sateesh Kumar, Shivin Dass, Georgios Pavlakos, Roberto Martín-Martín
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
In this work, we study the problem of data retrieval for few-shot imitation learning: selecting data from a large dataset to train a performant policy for a specific task, given only a few target demonstrations. Prior methods retrieve data using a single-feature distance heuristic, assuming that the best demonstrations are those that most closely resemble the target examples in visual, semantic, or motion space. However, this approach captures only a subset of the relevant information and can introduce detrimental demonstrations, e.g., retrieving data from unrelated tasks due to similar scene layouts, or selecting similar motions from tasks with divergent goals. We present COLLAGE, a method for COLLective data AGgrEgation in few-shot imitation learning that uses an adaptive late fusion mechanism to guide the selection of relevant demonstrations based on a task-specific combination of multiple cues. COLLAGE follows a simple, flexible, and efficient recipe: it assigns weights to subsets of the dataset that are pre-selected using a single feature (e.g., appearance, shape, or language similarity), based on how well a policy trained on each subset predicts actions in the target demonstrations. These weights are then used to perform importance sampling during policy training, sampling data more densely or sparsely according to estimated relevance. COLLAGE is general and feature-agnostic, allowing it to combine any number of subsets selected by any retrieval heuristic, and to identify which subsets provide the greatest benefit for the target task. In extensive experiments, COLLAGE outperforms state-of-the-art retrieval and multi-task learning approaches by 5.1% in simulation across 10 tasks, and by 16.6% in the real world across 6 tasks, where we perform retrieval from the large-scale DROID dataset. More information at https://robin-lab.cs.utexas.edu/COLLAGE .
