仮想から現実への深層強化学習：地図なしナビゲーションのための移動ロボットの連続制御
Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation
10次元の距離センサ情報と目標位置を入力に、強化学習で地図不要の連続操舵プランナを学習し、未知の仮想・実環境で衝突なく目標到達できることを示した。
著者: Lei Tai, Giuseppe Paolo, Ming Liu
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planner can be directly applied in unseen virtual and real environments. The experiments show that the proposed mapless motion planner can navigate the nonholonomic mobile robot to the desired targets without colliding with any obstacles.
