Retrieving target objects from unknown, confined spaces remains a challenging task that requires integrated, task-driven active sensing and rearrangement planning. Previous approaches have independently addressed active sensing and rearrangement planning, limiting their practicality in real-world scenarios. This paper presents a new, integrated heuristic-based active sensing and Monte-Carlo Tree Search (MCTS)-based retrieval planning approach. These components provide feedback to one another to actively sense critical, unobserved areas suitable for the retrieval planner to plan a sequence for relocating path-blocking obstacles and a collision-free trajectory for retrieving the target object. We demonstrate the effectiveness of our approach using a robot arm equipped with an in-hand camera in both simulated and real-world confined, cluttered scenarios. Our framework is compared against various state-of-the-art methods. The results indicate that our proposed approach outperforms baseline methods by a significant margin in terms of the success rate, the object rearrangement planning time consumption and the number of planning trials before successfully retrieving the target. Videos can be found at https://youtu.be/tea7I-3RtV0.

翻译：从未知、受限空间中检索目标物体仍然是一项具有挑战性的任务，需要集成任务驱动的主动感知与重排规划。以往的方法分别独立处理主动感知与重排规划，限制了其在真实场景中的实用性。本文提出了一种新的、基于启发式的主动感知与基于蒙特卡洛树搜索（MCTS）的检索规划集成方法。这些组件相互提供反馈，以主动感知对检索规划器至关重要的、未被观测的区域，从而规划出用于移开路径障碍物的操作序列以及用于检索目标物体的无碰撞轨迹。我们使用配备手内摄像头的机械臂，在模拟和真实的受限、杂乱场景中验证了所提方法的有效性。我们的框架与多种先进方法进行了比较。结果表明，在成功率、物体重排规划耗时以及成功检索目标前的规划尝试次数方面，我们提出的方法均显著优于基线方法。相关视频可见于 https://youtu.be/tea7I-3RtV0。