異種ロボットのデータからナビゲーションを学習する階層統合モデル
Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots
知覚モデルとダイナミクスモデルを別々に学習し、テスト時に階層的に統合して行動計画を行う強化学習手法HIntを提案。異なるロボットで収集したデータを活用しつつ、展開ロボットの物理能力を尊重した移動ナビゲーションを実現した。
著者: Katie Kang, Gregory Kahn, Sergey Levine
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Deep reinforcement learning algorithms require large and diverse datasets in order to learn successful policies for perception-based mobile navigation. However, gathering such datasets with a single robot can be prohibitively expensive. Collecting data with multiple different robotic platforms with possibly different dynamics is a more scalable approach to large-scale data collection. But how can deep reinforcement learning algorithms leverage such heterogeneous datasets? In this work, we propose a deep reinforcement learning algorithm with hierarchically integrated models (HInt). At training time, HInt learns separate perception and dynamics models, and at test time, HInt integrates the two models in a hierarchical manner and plans actions with the integrated model. This method of planning with hierarchically integrated models allows the algorithm to train on datasets gathered by a variety of different platforms, while respecting the physical capabilities of the deployment robot at test time. Our mobile navigation experiments show that HInt outperforms conventional hierarchical policies and single-source approaches.
