メタ学習による視覚ナビゲーションスキルの新しい観測への少数ショット適応
A Few Shot Adaptation of Visual Navigation Skills to New Observations using Meta-Learning
知覚ネットワークと推論ネットワークの間に潜在特徴を設け、推論側を固定したまま知覚側をメタ学習で素早く適応させることで、新しいセンサ構成や目標色に対して3ショットでナビゲーション方策を適応できる手法を提案した。
著者: Qian Luo, Maks Sorokin, Sehoon Ha
分類: cs.RO, cs.AI
原文アブストラクト
Target-driven visual navigation is a challenging problem that requires a robot to find the goal using only visual inputs. Many researchers have demonstrated promising results using deep reinforcement learning (deep RL) on various robotic platforms, but typical end-to-end learning is known for its poor extrapolation capability to new scenarios. Therefore, learning a navigation policy for a new robot with a new sensor configuration or a new target still remains a challenging problem. In this paper, we introduce a learning algorithm that enables rapid adaptation to new sensor configurations or target objects with a few shots. We design a policy architecture with latent features between perception and inference networks and quickly adapt the perception network via meta-learning while freezing the inference network. Our experiments show that our algorithm adapts the learned navigation policy with only three shots for unseen situations with different sensor configurations or different target colors. We also analyze the proposed algorithm by investigating various hyperparameters.
