Recently, a diverse range of robots with various functionalities have become a part of our daily lives. However, these robots either lack an arm or have less capable arms, mainly used for gestures. Another characteristic of the robots is that they are wheeled-type robots, restricting their operation to even surfaces. Several software platforms proposed in prior research have often focused on quadrupedal robots equipped with manipulators. However, many of these platforms lacked a comprehensive system combining perception, navigation, locomotion, and manipulation. This research introduces a software framework for clearing household objects with a quadrupedal robot. The proposed software framework utilizes the perception of the robot’s environment through sensor inputs and organizes household objects to their designated locations. The proposed framework was verified by experiments within a simulation environment resembling the conditions of the RoboCup@Home 2021-virtual competition involving variations in objects and poses, where outcomes demonstrate promising performance.

Robust manipulation of small, densely stacked objects remains a challenging problem due to severe occlusions and geometric ambiguities, particularly under single-view sensing conditions. When observed using a single RGB-D sensor, adjacent surfaces of featureless cuboid objects, such as Jenga blocks, often merge in depth measurements, making reliable instance separation and pose estimation difficult. This paper presents a strategy-driven perception and manipulation framework for the robotic rearrangement of randomly stacked Jenga blocks under single RGB-D sensor constraints. The proposed approach employs a heightmap-based perception pipeline that integrates color filtering with geometric reasoning to segment individual blocks and estimate manipulation-compatible poses. Beyond perception, the proposed system determines robot actions through a structured manipulation policy consisting of region-wise search for directly executable grasps, grasp candidate evaluation based on accessibility and collision risk, selective local regrasping for workspace reconfiguration, and placement mode selection between direct insertion and sliding-assisted placement. In this framework, controlled grasp-and-release actions are applied only when no directly executable candidate is found within the currently scanned region and a suitable recovery target can be identified, thereby transforming cluttered local arrangements into more executable states without requiring additional sensing modalities. Experimental results, conducted under competition-equivalent conditions, demonstrate a high task success rate of 99.02%, confirming the robustness and reliability of the proposed framework. The results show that strategy-driven manipulation can effectively compensate for perception limitations in single RGB-D sensor environments, enabling stable and efficient pick-and-place operations in dense clutter.