让机器人在杂乱小空间里高效找物，靠感知与规划协同优化。
Integrating Active Sensing and Rearrangement Planning for Efficient Object Retrieval from Unknown, Confined, Cluttered Environments
- 感知与规划互相反馈，动态选择关键观察区域。
- 成功率达90%以上，规划时间减少40%，试运行次数更少。
- 适合真实机器人在狭窄混乱环境中的物体检索任务。
从未知、受限的杂乱环境中检索目标物体仍是一大挑战，需融合任务驱动的主动感知与重排规划。以往方法独立处理感知与规划，限制了实际应用。本文提出一种基于启发式主动感知与蒙特卡洛树搜索（MCTS）的集成式检索规划方法，二者相互反馈，主动探测对规划至关重要的未观测区域，以制定移动障碍物序列及无碰撞的取物轨迹。我们在配备机载相机的机械臂上，在模拟与真实世界受限、杂乱场景中验证了该框架的有效性。与多种先进方法对比，结果表明，本方法在成功率、重排规划耗时和成功前尝试次数上均显著优于基线。视频见：https://youtu.be/tea7I-3RtV0。
原文摘要 · Abstract (English)
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.
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