Object Rearrangement

Object rearrangement research focuses on enabling robots to autonomously manipulate objects within a scene to achieve a desired configuration, often guided by human instructions or visual goals. Current efforts concentrate on developing robust and efficient algorithms, leveraging techniques like diffusion models, reinforcement learning, graph neural networks, and large language models to address challenges in object detection, pose estimation, task planning, and multi-robot coordination. This field is crucial for advancing robotics capabilities in various domains, including household assistance, warehouse automation, and human-robot collaboration, by enabling robots to perform complex manipulation tasks in unstructured environments.