Articulated Object Manipulation

Articulated object manipulation focuses on enabling robots to dexterously interact with objects possessing multiple interconnected parts, such as doors or drawers. Current research emphasizes developing robust methods for perceiving and modeling the object's 3D structure and articulation, often employing techniques like differentiable rendering, point cloud processing with models such as SAM2, and affordance learning to guide manipulation actions. These advancements leverage both simulation and real-world data, incorporating techniques like imitation learning, reinforcement learning, and large language models to improve generalization and efficiency. The ultimate goal is to create robots capable of handling a wide variety of articulated objects in unstructured environments, with significant implications for robotics, automation, and human-robot interaction.