We propose DeepExplorer, a simple and lightweight metric-free exploration method for topological mapping of unknown environments. It performs task and motion planning (TAMP) entirely in image feature space. The task planner is a recurrent network using the latest image observation sequence to hallucinate a feature as the next-best exploration goal. The motion planner then utilizes the current and the hallucinated features to generate an action taking the agent towards that goal. The two planners are jointly trained via deeply-supervised imitation learning from expert demonstrations. During exploration, we iteratively call the two planners to predict the next action, and the topological map is built by constantly appending the latest image observation and action to the map and using visual place recognition (VPR) for loop closing. The resulting topological map efficiently represents an environment's connectivity and traversability, so it can be used for tasks such as visual navigation. We show DeepExplorer's exploration efficiency and strong sim2sim generalization capability on large-scale simulation datasets like Gibson and MP3D. Its effectiveness is further validated via the image-goal navigation performance on the resulting topological map. We further show its strong zero-shot sim2real generalization capability in real-world experiments. The source code is available at \url{https://ai4ce.github.io/DeepExplorer/}.
翻译:我们提议深海勘探者,这是用于对未知环境进行地形制图的简单和轻量度的无标准勘探方法,它完全在图像特征空间中执行任务和运动规划(TAMP),任务规划器是一个经常性的网络,使用最新的图像观测序列,将某个特征幻化为下一个最佳勘探目标。然后,运动规划器利用当前和幻觉特征,以产生一种使代理人朝着这个目标前进的行动。这两位规划者通过从专家演示中深入监督的模拟学习,接受共同培训。在探索期间,我们反复地呼吁两位规划者预测下一个行动,而地形图则通过不断将最新图像观测和动作附在地图上,并利用视觉位置识别(VPR)来建立。由此形成的地形图有效地代表了环境的连通性和可移植性,因此可用于视觉导航等任务。我们展示了DeepExexExplorer的探索效率和在大型模拟数据集(如吉布斯和MP3D)上的强的Sim2Simbrial/simmission implication能力。我们进一步展示了它的有效性,在由此而成的顶层地图上展示了它具有强度的磁目标导航能力。</s>