We consider the problem of time-limited robotic exploration in previously unseen environments where exploration is limited by a predefined amount of time. We propose a novel exploration approach using learning-augmented model-based planning. We generate a set of subgoals associated with frontiers on the current map and derive a Bellman Equation for exploration with these subgoals. Visual sensing and advances in semantic mapping of indoor scenes are exploited for training a deep convolutional neural network to estimate properties associated with each frontier: the expected unobserved area beyond the frontier and the expected timesteps (discretized actions) required to explore it. The proposed model-based planner is guaranteed to explore the whole scene if time permits. We thoroughly evaluate our approach on a large-scale pseudo-realistic indoor dataset (Matterport3D) with the Habitat simulator. We compare our approach with classical and more recent RL-based exploration methods, demonstrating its clear advantages in several settings.
翻译:我们考虑了在以前看不见的环境中进行有时间限制的机器人探索的问题,在这种环境中,勘探受到预先确定的一定时间的限制。我们提议采用新颖的探索方法,采用学习强化模型规划。我们制作了一套与当前地图上的边界有关的次级目标,并用这些次级目标进行贝尔曼计算勘探。视觉遥感和室内景象的语义制图的进步被利用来训练一个深层革命神经网络,以估计与每个边界有关的特性:预期的边界以外的未观测区域,以及勘探所需的预期的时间步骤(分解的行动)。拟议的基于模型的规划员保证在时间允许的情况下对整个场进行探索。我们彻底评估了我们关于大型假现实室内数据集(塔道3D)和生境模拟器的方法。我们将我们的方法与传统和较近期的RL勘探方法进行比较,并表明其在若干环境中的明显优势。