Flexible State-Aware Planning for Robust Object Placement in Home Tidy-Up with Autonomous Mobile Manipulators

This paper aims to achieve flexible placement onto tray-type storage areas using a mobile manipulator. In a previous study, tray-packing was accomplished by preparing object masks in advance before tidy-up and planning the arrangement by matching these masks with the available tray space. However, such an approach faced difficulties in handling unknown objects and postures for which prior information could not be obtained, and was unable to take the current state of the tray into account, resulting in limited flexibility. The proposed system estimates masks for previously unregistered objects from an RGB-D image, verifies and corrects object posture to a default, builds a tray mask from the current state of the tray, and computes collision-aware, space-efficient placements via a 2D irregular packing algorithm. The proposed system performed a tidy-up on five objects placed in various postures, achieving a success rate of 93%. These results indicate improved flexibility, robustness, and practicality for real-world tidy-up compared to the previous system.