Multi Object Rearrangement

Multi-object rearrangement focuses on developing robotic systems capable of efficiently and accurately manipulating multiple objects to achieve a desired configuration. Current research emphasizes improving planning algorithms, often employing graph search techniques, Monte Carlo tree search, or deep reinforcement learning, to handle complex scenarios with diverse object types and constraints, including limited robot capabilities and partially observable environments. This field is crucial for advancing robotics in areas like warehouse automation and household assistance, where robots need to adapt to unstructured environments and user preferences, often incorporating visual preference learning and semantic understanding to achieve human-aligned results.