FlowBotHD: History-Aware Diffuser Handling Ambiguities in Articulated Objects Manipulation
<|reference_start|>FlowBotHD: History-Aware Diffuser Handling Ambiguities in Articulated Objects Manipulation: We introduce a novel approach to manipulate articulated objects with ambiguities, such as opening a door, in which multi-modality and occlusions create ambiguities about the opening side and direction. Multi-modality occurs when the method to open a fully closed door (push, pull, slide) is uncertain, or the side from which it should be opened is uncertain. Occlusions further obscure the door's shape from certain angles, creating further ambiguities during the occlusion. To tackle these challenges, we propose a history-aware diffusion network that models the multi-modal distribution of the articulated object and uses history to disambiguate actions and make stable predictions under occlusions. Experiments and analysis demonstrate the state-of-art performance of our method and specifically improvements in ambiguity-caused failure modes. Our project website is available at https://flowbothd.github.io/.<|reference_end|>
@article{li2024flowbothd:,
    title={FlowBotHD: History-Aware Diffuser Handling Ambiguities in Articulated
  Objects Manipulation},
    author={Yishu Li, Wen Hui Leng, Yiming Fang, Ben Eisner, David Held},
    journal={arXiv preprint arXiv:2410.07078},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07078},
    primaryClass={cs.RO}
}