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English(EN) Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models

Mamba Policy 以更少的参数提供高效的3D操作

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为Mamba Policy的新AI策略模型，该模型显著降低了3D操作任务的计算需求。该模型采用混合方法，在其XMamba Block中结合了Mamba和Attention机制，与传统的U-Net骨干网络相比，参数数量减少了80%以上，同时提高了性能。在Adroit、Dexart和MetaWorld等数据集上的实验证明了Mamba Policy的效率和增强的鲁棒性，尤其是在长时域场景中。 AI

影响 该模型的效率有望在资源受限的设备上实现更复杂的3D操作任务。

排序理由 介绍新模型架构的研究论文。[lever_c_research降级：ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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Mamba Policy 以更少的参数提供高效的3D操作

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介绍新模型架构的研究论文。[lever_c_research降级：ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jiahang Cao, Qiang Zhang, Jingkai Sun, Jiaxu Wang, Hao Cheng, Yulin Li, Jun Ma, Kun Wu, Zhiyuan Xu, Yecheng Shao, Wen Zhao, Gang Han, Yijie Guo, Renjing Xu ·

    Mamba Policy：迈向具有混合选择性状态模型的 eficient 3D 扩散策略

    arXiv:2409.07163v3 Announce Type: replace-cross Abstract: Diffusion models have been widely employed in the field of 3D manipulation due to their efficient capability to learn distributions, allowing for precise prediction of action trajectories. However, diffusion models typical…

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