Procedural Generation of Articulated Simulation-Ready Assets
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918174201479168 |
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| author | Joshi, Abhishek Han, Beining Nugent, Jack Saez-Diez, Max Gonzalez Zuo, Yiming Liu, Jonathan Wen, Hongyu Alexandropoulos, Stamatis Kayan, Karhan Calveri, Anna Sun, Tao Liu, Gaowen Shao, Yi Raistrick, Alexander Deng, Jia |
| author_facet | Joshi, Abhishek Han, Beining Nugent, Jack Saez-Diez, Max Gonzalez Zuo, Yiming Liu, Jonathan Wen, Hongyu Alexandropoulos, Stamatis Kayan, Karhan Calveri, Anna Sun, Tao Liu, Gaowen Shao, Yi Raistrick, Alexander Deng, Jia |
| contents | We introduce Infinigen-Articulated, a toolkit for generating realistic, procedurally generated articulated assets for robotics simulation. We include procedural generators for 18 common articulated object categories along with high-level utilities for use creating custom articulated assets in Blender. We also provide an export pipeline to integrate the resulting assets along with their physical properties into common robotics simulators. Experiments demonstrate that assets sampled from these generators are effective for movable object segmentation, training generalizable reinforcement learning policies, and sim-to-real transfer of imitation learning policies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10755 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Procedural Generation of Articulated Simulation-Ready Assets Joshi, Abhishek Han, Beining Nugent, Jack Saez-Diez, Max Gonzalez Zuo, Yiming Liu, Jonathan Wen, Hongyu Alexandropoulos, Stamatis Kayan, Karhan Calveri, Anna Sun, Tao Liu, Gaowen Shao, Yi Raistrick, Alexander Deng, Jia Robotics Graphics We introduce Infinigen-Articulated, a toolkit for generating realistic, procedurally generated articulated assets for robotics simulation. We include procedural generators for 18 common articulated object categories along with high-level utilities for use creating custom articulated assets in Blender. We also provide an export pipeline to integrate the resulting assets along with their physical properties into common robotics simulators. Experiments demonstrate that assets sampled from these generators are effective for movable object segmentation, training generalizable reinforcement learning policies, and sim-to-real transfer of imitation learning policies. |
| title | Procedural Generation of Articulated Simulation-Ready Assets |
| topic | Robotics Graphics |
| url | https://arxiv.org/abs/2505.10755 |