逆生成モデリングによる少数ショットタスク学習
Few-Shot Task Learning through Inverse Generative Modeling
事前学習した可逆生成モデルを使い、重み更新なしで数例から新しいタスク概念（目標や動作スタイル）を学習する手法を提案。物体再配置・ナビゲーション・動作キャプション・自動運転・実機卓上操作で検証。
著者: Aviv Netanyahu, Yilun Du, Antonia Bronars, Jyothish Pari, Joshua Tenenbaum, Tianmin Shu, Pulkit Agrawal
分類: cs.AI, cs.LG, cs.RO
原文アブストラクト
Learning the intents of an agent, defined by its goals or motion style, is often extremely challenging from just a few examples. We refer to this problem as task concept learning and present our approach, Few-Shot Task Learning through Inverse Generative Modeling (FTL-IGM), which learns new task concepts by leveraging invertible neural generative models. The core idea is to pretrain a generative model on a set of basic concepts and their demonstrations. Then, given a few demonstrations of a new concept (such as a new goal or a new action), our method learns the underlying concepts through backpropagation without updating the model weights, thanks to the invertibility of the generative model. We evaluate our method in five domains -- object rearrangement, goal-oriented navigation, motion caption of human actions, autonomous driving, and real-world table-top manipulation. Our experimental results demonstrate that via the pretrained generative model, we successfully learn novel concepts and generate agent plans or motion corresponding to these concepts in (1) unseen environments and (2) in composition with training concepts.
