Tabletop Rearrangement

Tabletop rearrangement research focuses on efficiently and optimally planning robot actions to reorganize objects on a surface, considering various manipulation primitives (e.g., picking, pushing) and object dependencies. Current work explores algorithms like hierarchical best-first search and Monte Carlo tree search to generate high-quality action sequences, often incorporating machine learning models (e.g., neural networks) to optimize for criteria like minimizing buffer space or achieving human-defined notions of "tidiness." These advancements are significant for robotics, improving task planning speed and success rates in applications such as automated warehouse organization and assistive robotics.