This paper presents Rummaging Using Mutual Information (RUMI), a method for online generation of robot action sequences to gather information about the pose of a known movable object in visually-occluded environments. Focusing on contact-rich rummaging, our approach leverages mutual information between the object pose distribution and robot trajectory for action planning. From an observed partial point cloud, RUMI deduces the compatible object pose distribution and approximates the mutual information of it with workspace occupancy in real time. Based on this, we develop an information gain cost function and a reachability cost function to keep the object within the robot's reach. These are integrated into a model predictive control (MPC) framework with a stochastic dynamics model, updating the pose distribution in a closed loop. Key contributions include a new belief framework for object pose estimation, an efficient information gain computation strategy, and a robust MPC-based control scheme. RUMI demonstrates superior performance in both simulated and real tasks compared to baseline methods.

翻译：本文提出了一种基于互信息的翻找探索方法（RUMI），用于在线生成机器人动作序列，以在视觉遮挡环境中收集已知可移动物体的位姿信息。针对接触密集型的翻找任务，本方法利用物体位姿分布与机器人轨迹之间的互信息进行动作规划。RUMI从观测到的局部点云出发，推断出兼容的物体位姿分布，并实时近似其与工作空间占据状态的互信息。基于此，我们构建了信息增益代价函数和可达性代价函数，以确保物体保持在机器人可操作范围内。这些函数被整合到具有随机动力学模型的模型预测控制（MPC）框架中，以闭环方式更新位姿分布。主要贡献包括：一种新的物体位姿估计置信度框架、一种高效的信息增益计算策略，以及一种基于MPC的鲁棒控制方案。与基线方法相比，RUMI在仿真和实际任务中均表现出更优越的性能。