FlowBotHD: History-Aware Diffuser Handling Ambiguities in Articulated Objects Manipulation
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929650927665152 |
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| author | Li, Yishu Leng, Wen Hui Fang, Yiming Eisner, Ben Held, David |
| author_facet | Li, Yishu Leng, Wen Hui Fang, Yiming Eisner, Ben Held, David |
| contents | We introduce a novel approach for manipulating articulated objects which are visually ambiguous, such doors which are symmetric or which are heavily occluded. These ambiguities can cause uncertainty over different possible articulation modes: for instance, when the articulation direction (e.g. push, pull, slide) or location (e.g. left side, right side) of a fully closed door are uncertain, or when distinguishing features like the plane of the door are occluded due to the viewing angle. To tackle these challenges, we propose a history-aware diffusion network that can model multi-modal distributions over articulation modes for articulated objects; our method further uses observation history to distinguish between modes and make stable predictions under occlusions. Experiments and analysis demonstrate that our method achieves state-of-art performance on articulated object manipulation and dramatically improves performance for articulated objects containing visual ambiguities. Our project website is available at https://flowbothd.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07078 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FlowBotHD: History-Aware Diffuser Handling Ambiguities in Articulated Objects Manipulation Li, Yishu Leng, Wen Hui Fang, Yiming Eisner, Ben Held, David Robotics We introduce a novel approach for manipulating articulated objects which are visually ambiguous, such doors which are symmetric or which are heavily occluded. These ambiguities can cause uncertainty over different possible articulation modes: for instance, when the articulation direction (e.g. push, pull, slide) or location (e.g. left side, right side) of a fully closed door are uncertain, or when distinguishing features like the plane of the door are occluded due to the viewing angle. To tackle these challenges, we propose a history-aware diffusion network that can model multi-modal distributions over articulation modes for articulated objects; our method further uses observation history to distinguish between modes and make stable predictions under occlusions. Experiments and analysis demonstrate that our method achieves state-of-art performance on articulated object manipulation and dramatically improves performance for articulated objects containing visual ambiguities. Our project website is available at https://flowbothd.github.io/. |
| title | FlowBotHD: History-Aware Diffuser Handling Ambiguities in Articulated Objects Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2410.07078 |