Dyna-LfLH: 学習された幻覚による動的環境での俊敏なナビゲーション学習
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination
過去の成功体験から動的障害物を潜在分布で生成する自己教師あり学習により、動的環境での安全な経路計画を実現し、成功率を最大25%向上させた。
著者: Saad Abdul Ghani, Zizhao Wang, Peter Stone, Xuesu Xiao
分類: cs.RO, cs.LG
原文アブストラクト
This paper introduces Dynamic Learning from Learned Hallucination (Dyna-LfLH), a self-supervised method for training motion planners to navigate environments with dense and dynamic obstacles. Classical planners struggle with dense, unpredictable obstacles due to limited computation, while learning-based planners face challenges in acquiring high-quality demonstrations for imitation learning or dealing with exploration inefficiencies in reinforcement learning. Building on Learning from Hallucination (LfH), which synthesizes training data from past successful navigation experiences in simpler environments, Dyna-LfLH incorporates dynamic obstacles by generating them through a learned latent distribution. This enables efficient and safe motion planner training. We evaluate Dyna-LfLH on a ground robot in both simulated and real environments, achieving up to a 25% improvement in success rate compared to baselines.
