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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.17070 |
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| _version_ | 1866915460115595264 |
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| author | Kadi, Halid Abdulrahim Terzić, Kasim |
| author_facet | Kadi, Halid Abdulrahim Terzić, Kasim |
| contents | We present a novel goal-conditioned recurrent state space (GC-RSSM) model capable of learning latent dynamics of pick-and-place garment manipulation. Our proposed method LaGarNet matches the state-of-the-art performance of mesh-based methods, marking the first successful application of state-space models on complex garments. LaGarNet trains on a coverage-alignment reward and a dataset collected through a general procedure supported by a random policy and a diffusion policy learned from few human demonstrations; it substantially reduces the inductive biases introduced in the previous similar methods. We demonstrate that a single-policy LaGarNet achieves flattening on four different types of garments in both real-world and simulation settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17070 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LaGarNet: Goal-Conditioned Recurrent State-Space Models for Pick-and-Place Garment Flattening Kadi, Halid Abdulrahim Terzić, Kasim Robotics We present a novel goal-conditioned recurrent state space (GC-RSSM) model capable of learning latent dynamics of pick-and-place garment manipulation. Our proposed method LaGarNet matches the state-of-the-art performance of mesh-based methods, marking the first successful application of state-space models on complex garments. LaGarNet trains on a coverage-alignment reward and a dataset collected through a general procedure supported by a random policy and a diffusion policy learned from few human demonstrations; it substantially reduces the inductive biases introduced in the previous similar methods. We demonstrate that a single-policy LaGarNet achieves flattening on four different types of garments in both real-world and simulation settings. |
| title | LaGarNet: Goal-Conditioned Recurrent State-Space Models for Pick-and-Place Garment Flattening |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.17070 |