论文标题
土地：学会从脱离工程中导航
LaND: Learning to Navigate from Disengagements
论文作者
论文摘要
在现实世界中，一贯测试自动移动机器人是开发自主导航系统的必要方面。每当人类安全监护仪执行不良操作的机器人，人体安全监护仪都会脱离机器人的自主系统时，自治开发人员就会深入了解如何改善自主权系统。但是，我们认为这些解开不仅显示系统失败的位置，这对于故障排除非常有用，而且还提供了一个直接的学习信号，机器人可以通过该信号学会导航。我们提出了一种学习方法，用于学习从脱离或土地中导航。 Land学习了一个神经网络模型，该模型可以预测鉴于当前的感官观察，然后在测试时间计划并执行避免脱离脱离的行动的情况下导致了哪些行动导致脱离接触。我们的结果表明，土地可以成功地学习在不同的现实世界人行道环境中导航，从而超过了模仿学习和强化学习方法。视频，代码和其他材料可在我们的网站上找到https://sites.google.com/view/sidewalk-learning
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
