Object Rearrangement Planning

Object rearrangement planning focuses on developing algorithms that enable robots to efficiently move and organize objects within an environment, often to achieve a specified goal configuration. Current research emphasizes efficient planning strategies for multi-object scenarios, particularly in confined or cluttered spaces, employing techniques like genetic algorithms, Monte Carlo Tree Search, and deep reinforcement learning, often incorporating multi-agent coordination and language-guided instruction. These advancements are crucial for improving robotic manipulation capabilities in various applications, including warehouse automation, household robotics, and search-and-rescue operations.