  Object search is a fundamental skill for household robots, yet the core
problem lies in the robot's ability to locate the target object accurately. The
dynamic nature of household environments, characterized by the arbitrary
placement of daily objects by users, makes it challenging to perform target
localization. To efficiently locate the target object, the robot needs to be
equipped with knowledge at both the object and room level. However, existing
approaches rely solely on one type of knowledge, leading to unsatisfactory
object localization performance and, consequently, inefficient object search
processes. To address this problem, we propose a commonsense scene graph-based
target localization, CSG-TL, to enhance target object search in the household
environment. Given the pre-built map with stationary items, the robot models
the room-level knowledge with object-level commonsense knowledge generated by a
large language model (LLM) to a commonsense scene graph (CSG), supporting both
types of knowledge for CSG-TL. To demonstrate the superiority of CSG-TL on
target localization, extensive experiments are performed on the real-world
ScanNet dataset and the AI2THOR simulator. Moreover, we have extended CSG-TL to
an object search framework, CSG-OS, validated in both simulated and real-world
environments. Code and videos are available at
https://sites.google.com/view/csg-os.
