Abstract--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. Active exploration, the process of autonomously planning actions to gather more information about a target quantity, is a core problem in robotics, particularly when dealing with unknown environments [2]. This problem encompasses a range of scenarios, differentiated by the type of robot (e.g., mobile Figure 1: (a) A real-world active exploration experiment where vs. stationary), the primary sensor modality (often vision), and the goal is to estimate the pose of a movable mug. The the specific quantity to be estimated. Observed surface points are in red.