论文标题
安全的，闭塞意识的操纵在线对象重建狭窄的空间
Safe, Occlusion-Aware Manipulation for Online Object Reconstruction in Confined Spaces
论文作者
论文摘要
机器人操纵的最新工作集中在遮挡下混乱空间中的物体检索。然而，大多数努力都缺乏对方法完整性的条件分析，或者仅在可以从工作空间中删除对象时，这些方法仅适用。这项工作制定了一般，闭塞感知的操作任务，并专注于在限制空间内与现场重排的安全对象重建。它提出了一个框架，可确保安全性保证。此外，通过与在模拟中随机生成的实验中的随机和贪婪的基线进行比较，从经验上开发和评估了这种单调实例的抽象框架的实例化。即使对于具有逼真物体的混乱场景，提议的算法也显着胜过基准，并在实验条件下保持较高的成功率。
Recent work in robotic manipulation focuses on object retrieval in cluttered spaces under occlusion. Nevertheless, the majority of efforts lack an analysis of conditions for the completeness of the approaches or the methods apply only when objects can be removed from the workspace. This work formulates the general, occlusion-aware manipulation task, and focuses on safe object reconstruction in a confined space with in-place rearrangement. It proposes a framework that ensures safety with completeness guarantees. Furthermore, an algorithm, which is an instantiation of this abstract framework for monotone instances is developed and evaluated empirically by comparing against a random and a greedy baseline on randomly generated experiments in simulation. Even for cluttered scenes with realistic objects, the proposed algorithm significantly outperforms the baselines and maintains a high success rate across experimental conditions.
