生の深度入力から社会規範に沿うナビゲーションを実現する生成的敵対的模倣学習
Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
GAILを用いて、歩行者位置などの事前情報なしに生の深度画像のみから社会的に配慮した移動ロボットのナビゲーションを学習・実時間実行する手法を提案した。
著者: Lei Tai, Jingwei Zhang, Ming Liu, Wolfram Burgard
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
We present an approach for mobile robots to learn to navigate in dynamic environments with pedestrians via raw depth inputs, in a socially compliant manner. To achieve this, we adopt a generative adversarial imitation learning (GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our approach overcomes the disadvantages of previous methods, as they heavily depend on the full knowledge of the location and velocity information of nearby pedestrians, which not only requires specific sensors, but also the extraction of such state information from raw sensory input could consume much computation time. In this paper, our proposed GAIL-based model performs directly on raw depth inputs and plans in real-time. Experiments show that our GAIL-based approach greatly improves the safety and efficiency of the behavior of mobile robots from pure behavior cloning. The real-world deployment also shows that our method is capable of guiding autonomous vehicles to navigate in a socially compliant manner directly through raw depth inputs. In addition, we release a simulation plugin for modeling pedestrian behaviors based on the social force model.
