Viewpoint planning is an important task in any application where objects or scenes need to be viewed from different angles to achieve sufficient coverage. The mapping of confined spaces such as shelves is an especially challenging task since objects occlude each other and the scene can only be observed from the front, posing limitations on the possible viewpoints. In this paper, we propose a deep reinforcement learning framework that generates promising views aiming at reducing the map entropy. Additionally, the pipeline extends standard viewpoint planning by predicting adequate minimally invasive push actions to uncover occluded objects and increase the visible space. Using a 2.5D occupancy height map as state representation that can be efficiently updated, our system decides whether to plan a new viewpoint or perform a push. To learn feasible pushes, we use a neural network to sample push candidates on the map based on training data provided by human experts. As simulated and real-world experimental results with a robotic arm show, our system is able to significantly increase the mapped space compared to different baselines, while the executed push actions highly benefit the viewpoint planner with only minor changes to the object configuration.

翻译：视点规划是任何需要从不同角度观察物体或场景以实现充分覆盖的重要任务。货架等受限空间建图尤为困难，因为物体相互遮挡且仅能从正面观察场景，这限制了可行视点。本文提出一种深度强化学习框架，通过生成高价值视点来降低地图熵值。此外，该流程通过预测适当的微创推动动作来拓展标准视点规划，以揭示遮挡物体并增加可见空间。系统采用可高效更新的2.5D占据高度图作为状态表征，自主决策是规划新视点还是执行推动操作。为学习可行推动动作，我们基于人类专家提供的训练数据，利用神经网络在地图上采样候选推动点。通过机械臂的仿真与实物实验验证，与多种基线方法相比，本系统能显著增加建图空间，且执行的推动操作仅对物体配置产生微小改变，却极大提升了视点规划器的性能。