介入から学ぶナビゲーション学習
LaND: Learning to Navigate from Disengagements
人間による安全介入（ディスエンゲージ）を学習信号として活用し、強化学習で屋外歩道を自律走行するナビゲーション手法を提案した論文。
著者: Gregory Kahn, Pieter Abbeel, Sergey Levine
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
Consistently testing autonomous mobile robots in real world scenarios is a necessary aspect of developing autonomous navigation systems. Each time the human safety monitor disengages the robot's autonomy system due to the robot performing an undesirable maneuver, the autonomy developers gain insight into how to improve the autonomy system. However, we believe that these disengagements not only show where the system fails, which is useful for troubleshooting, but also provide a direct learning signal by which the robot can learn to navigate. We present a reinforcement learning approach for learning to navigate from disengagements, or LaND. LaND learns a neural network model that predicts which actions lead to disengagements given the current sensory observation, and then at test time plans and executes actions that avoid disengagements. Our results demonstrate LaND can successfully learn to navigate in diverse, real world sidewalk environments, outperforming both imitation learning and reinforcement learning approaches. Videos, code, and other material are available on our website https://sites.google.com/view/sidewalk-learning
