中レベル視覚表現が視覚運動ポリシーの汎化とサンプル効率を改善する
Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies
距離推定やエッジ検出などの中レベル知覚スキルを強化学習に組み込むことで、生画像からの学習よりナビゲーションタスクの汎化性能とサンプル効率が向上することを示し、効率的な特徴セットを提案した。
著者: Alexander Sax, Bradley Emi, Amir R. Zamir, Leonidas Guibas, Silvio Savarese, Jitendra Malik
分類: cs.CV, cs.AI, cs.LG, cs.NE, cs.RO
原文アブストラクト
How much does having visual priors about the world (e.g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e.g. delivering a package)? We study this question by integrating a generic perceptual skill set (e.g. a distance estimator, an edge detector, etc.) within a reinforcement learning framework--see Figure 1. This skill set (hereafter mid-level perception) provides the policy with a more processed state of the world compared to raw images. We find that using a mid-level perception confers significant advantages over training end-to-end from scratch (i.e. not leveraging priors) in navigation-oriented tasks. Agents are able to generalize to situations where the from-scratch approach fails and training becomes significantly more sample efficient. However, we show that realizing these gains requires careful selection of the mid-level perceptual skills. Therefore, we refine our findings into an efficient max-coverage feature set that can be adopted in lieu of raw images. We perform our study in completely separate buildings for training and testing and compare against visually blind baseline policies and state-of-the-art feature learning methods.
