Articulated Object

Articulated object manipulation focuses on enabling robots to effectively interact with objects possessing multiple interconnected parts, such as doors or drawers. Current research emphasizes developing robust methods for 3D reconstruction and pose estimation of these objects, often employing neural implicit representations, diffusion models, and graph-based approaches to capture complex geometries and motions. This research is crucial for advancing robotics, particularly in areas like human-robot interaction and autonomous manipulation in unstructured environments, by enabling robots to perform more complex and versatile tasks. The development of generalizable methods that handle diverse object types and noisy real-world data remains a key challenge and focus.