One-shot Video Imitation via Parameterized Symbolic Abstraction Graphs
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914954230104064 |
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| author | Wang, Jianren Liu, Kangni Guo, Dingkun Zhou, Xian Atkeson, Christopher G |
| author_facet | Wang, Jianren Liu, Kangni Guo, Dingkun Zhou, Xian Atkeson, Christopher G |
| contents | Learning to manipulate dynamic and deformable objects from a single demonstration video holds great promise in terms of scalability. Previous approaches have predominantly focused on either replaying object relationships or actor trajectories. The former often struggles to generalize across diverse tasks, while the latter suffers from data inefficiency. Moreover, both methodologies encounter challenges in capturing invisible physical attributes, such as forces. In this paper, we propose to interpret video demonstrations through Parameterized Symbolic Abstraction Graphs (PSAG), where nodes represent objects and edges denote relationships between objects. We further ground geometric constraints through simulation to estimate non-geometric, visually imperceptible attributes. The augmented PSAG is then applied in real robot experiments. Our approach has been validated across a range of tasks, such as Cutting Avocado, Cutting Vegetable, Pouring Liquid, Rolling Dough, and Slicing Pizza. We demonstrate successful generalization to novel objects with distinct visual and physical properties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12674 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | One-shot Video Imitation via Parameterized Symbolic Abstraction Graphs Wang, Jianren Liu, Kangni Guo, Dingkun Zhou, Xian Atkeson, Christopher G Robotics Computer Vision and Pattern Recognition Learning to manipulate dynamic and deformable objects from a single demonstration video holds great promise in terms of scalability. Previous approaches have predominantly focused on either replaying object relationships or actor trajectories. The former often struggles to generalize across diverse tasks, while the latter suffers from data inefficiency. Moreover, both methodologies encounter challenges in capturing invisible physical attributes, such as forces. In this paper, we propose to interpret video demonstrations through Parameterized Symbolic Abstraction Graphs (PSAG), where nodes represent objects and edges denote relationships between objects. We further ground geometric constraints through simulation to estimate non-geometric, visually imperceptible attributes. The augmented PSAG is then applied in real robot experiments. Our approach has been validated across a range of tasks, such as Cutting Avocado, Cutting Vegetable, Pouring Liquid, Rolling Dough, and Slicing Pizza. We demonstrate successful generalization to novel objects with distinct visual and physical properties. |
| title | One-shot Video Imitation via Parameterized Symbolic Abstraction Graphs |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.12674 |