FlowBotHD：面向关节物体操作中歧义处理的历史感知扩散器
FlowBotHD: History-Aware Diffuser Handling Ambiguities in Articulated Objects Manipulation
AI总结：
本文提出历史感知扩散网络FlowBotHD，通过建模关节模式的多模态分布并利用观测历史，解决对称或遮挡导致的关节物体操作歧义，显著提升操作性能。
摘要：
我们提出了一种用于操作视觉上具有歧义的关节物体的新方法，例如对称或被严重遮挡的门。这些歧义可能导致对不同可能关节模式的不确定性：例如，当完全关闭的门的关节方向（如推、拉、滑动）或位置（如左侧、右侧）不确定时，或者当门的平面等区分性特征因视角而被遮挡时。为应对这些挑战，我们提出了一种历史感知扩散网络，该网络能够对关节物体的关节模式进行多模态分布建模；我们的方法进一步利用观测历史来区分不同模式，并在遮挡情况下做出稳定预测。实验与分析表明，我们的方法在关节物体操作上取得了最先进的性能，并显著提升了包含视觉歧义的关节物体的操作表现。我们的项目网站可在 https://flowbothd.github.io/ 访问。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
