HMT-Grasp：一种用于杂乱环境中机器人抓取的混合Mamba-Transformer方法
HMT-Grasp: A Hybrid Mamba-Transformer Approach for Robot Grasping in Cluttered Environments
AI总结：
本文提出HMT-Grasp，一种融合Vision Mamba与并行卷积-Transformer模块的混合架构，通过同时捕获全局和局部信息提升杂乱环境下的机器人抓取适应性、精度和灵活性，并在多个数据集及仿真与真实机器人实验中验证了其优于现有方法的性能。
摘要：
机器人抓取，无论是处理孤立物体、杂乱物品还是堆叠物体，在工业和服务应用中都起着关键作用。然而，当前基于卷积神经网络（CNNs）和视觉Transformer（ViTs）的视觉抓取检测方法往往难以适应多样化的场景，因为它们倾向于仅强调局部或全局特征，忽略了互补线索。在本文中，我们提出了一种新颖的混合Mamba-Transformer方法来解决这些挑战。我们的方法通过集成Vision Mamba和并行卷积-Transformer模块，有效捕获全局和局部信息，从而改进机器人视觉抓取。这种混合架构显著提高了各种机器人任务中的适应性、精度和灵活性。为了确保公平评估，我们在Cornell、Jacquard和OCID-Grasp数据集上进行了从简单到复杂场景的广泛实验。此外，我们还进行了仿真和真实机器人实验。结果表明，我们的方法不仅在标准抓取数据集上超越了最先进的技术，而且在仿真和真实机器人应用中都表现出强大的性能。
英文摘要：
Robot grasping, whether handling isolated objects, cluttered items, or stacked objects, plays a critical role in industrial and service applications. However, current visual grasp detection methods based on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) often struggle to adapt to diverse scenarios, as they tend to emphasize either local or global features exclusively, neglecting complementary cues. In this paper, we propose a novel hybrid Mamba-Transformer approach to address these challenges. Our method improves robotic visual grasping by effectively capturing both global and local information through the integration of Vision Mamba and parallel convolutional-transformer blocks. This hybrid architecture significantly improves adaptability, precision, and flexibility across various robotic tasks. To ensure a fair evaluation, we conducted extensive experiments on the Cornell, Jacquard, and OCID-Grasp datasets, ranging from simple to complex scenarios. Additionally, we performed both simulated and real-world robotic experiments. The results demonstrate that our method not only surpasses state-of-the-art techniques on standard grasping datasets but also delivers strong performance in both simulation and real-world robot applications.
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。
