The paper introduces GAMMA, a framework for Generalizable Articulation Modeling and Manipulating for Articulated Objects. It addresses challenges in manipulating articulated objects by learning articulation modeling and grasp pose affordance from diverse objects. GAMMA significantly outperforms existing algorithms in unseen and cross-category articulated objects, showcasing its generalizability and effectiveness in real-world scenarios.
The authors highlight the importance of understanding the physical structure of articulated objects to facilitate manipulation tasks. They propose a novel approach that leverages point cloud data to segment articulated parts, estimate joint parameters, and predict grasp pose affordance. By iteratively updating articulation parameters based on actual trajectories, GAMMA enhances modeling accuracy and manipulation success rates.
Experiments conducted in simulation environments demonstrate the superior performance of GAMMA compared to baseline methods like RL(TD3), Where2Act, and VAT-Mart. The results show that GAMMA excels in both articulation modeling accuracy and manipulation success rates across various tasks involving different categories of articulated objects.
Real-world experiments validate the generalization ability of GAMMA by applying it to manipulate cabinet drawers and doors as well as microwave doors. The framework proves effective in real-world robotic manipulation tasks, showcasing its practical applicability beyond simulated environments.
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by Qiaojun Yu,J... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2309.16264.pdfDeeper Inquiries