移動ロボットナビゲーションのための生涯学習アプローチ
A Lifelong Learning Approach to Mobile Robot Navigation
移動ロボットが自身の経験のみからナビゲーション行動を改善し、新しい環境で学習した後も過去の環境での能力を保持する生涯学習フレームワークLLfNを提案し、限られたメモリと計算資源の実機上で実装・検証した。
著者: Bo Liu, Xuesu Xiao, Peter Stone
分類: cs.RO
原文アブストラクト
This paper presents a self-improving lifelong learning framework for a mobile robot navigating in different environments. Classical static navigation methods require environment-specific in-situ system adjustment, e.g. from human experts, or may repeat their mistakes regardless of how many times they have navigated in the same environment. Having the potential to improve with experience, learning-based navigation is highly dependent on access to training resources, e.g. sufficient memory and fast computation, and is prone to forgetting previously learned capability, especially when facing different environments. In this work, we propose Lifelong Learning for Navigation (LLfN) which (1) improves a mobile robot's navigation behavior purely based on its own experience, and (2) retains the robot's capability to navigate in previous environments after learning in new ones. LLfN is implemented and tested entirely onboard a physical robot with a limited memory and computation budget.
