AAOPL: Automated Articulated Object Parameter Learning for Open-World Robotics

Abstract

Articulated objects are ubiquitous in daily environments, and effective manipulation of these objects is essential for advancing open-world robotics. Existing approaches, which rely heavily on large-scale data collection or simulation, often face limitations in real-world applications, including issues with generalization and the sim-to-real gap. In this paper, we introduce the Automated Articulated Object Parameter Learning (AAOPL) framework, which autonomously learns the articulation parameters of real-world articulated objects through direct interaction. This approach enables robots to generate precise manipulation trajectories without relying on predefined object models or extensive human demonstration data. To accelerate the learning process, we develop Accelerated Single-Step Gradient (ASSG) algorithm, which efficiently refines the articulation parameters by leveraging real-time execution feedback. Experimental results demonstrate that AAOPL can learn accurate articulation parameters within 30 minutes (80 trials) and generate robust manipulation trajectories, outperforming baseline methods in terms of both task completion and force efficiency. Our approach eliminates the need for large-scale training datasets and can adapt to various articulated objects in real-world environments, offering a scalable solution for autonomous robotic manipulation in unstructured settings.