基于MAD-CNN（模块化注意力扩张卷积神经网络）的可变刚度驱动机器人鲁棒碰撞检测
Robust Collision Detection for Robots with Variable Stiffness Actuation by Using MAD-CNN: Modularized-Attention-Dilated Convolutional Neural Network
AI总结：
针对可变刚度驱动协作机器人缺乏适配的基于学习的碰撞检测方法的问题，提出融合双归纳偏置与注意力模块的MAD-CNN，仅用4分钟最高刚度数据训练，就在多刚度下实现低延迟全检测，灵敏度和鲁棒性超现有SOTA模型。
摘要：
在协作机器人领域，确保安全至关重要，可降低人员受伤和环境受损的风险。除了避障之外，机器人还必须能够快速检测并应对意外碰撞。虽然已有多种基于学习的碰撞检测方法被提出，作为纯基于模型的检测技术的替代方案，但目前尚缺乏专为配备可变刚度驱动器的协作机器人设计的此类方法。此外，网络的鲁棒性还有进一步提升的空间，数据训练效率也有待提高。\n本文提出了一种新型网络——模块化注意力扩张卷积神经网络（MAD-CNN），用于配备可变刚度驱动器的机器人的碰撞检测。我们的模型融合了双归纳偏置机制和注意力模块，以提升数据效率并增强鲁棒性。具体而言，MAD-CNN仅使用时长4分钟、聚焦于关节最高刚度水平的碰撞数据集进行训练。尽管训练数据有限，MAD-CNN仍能在各种刚度条件下以极小的检测延迟鲁棒地检测出所有碰撞。此外，它还表现出更高的碰撞灵敏度，这有助于有效处理误报问题——而误报正是基于学习的方法中普遍存在的问题。实验结果表明，所提出的MAD-CNN模型在碰撞灵敏度和鲁棒性方面均优于现有的最先进模型。
英文摘要：
Ensuring safety is paramount in the field of collaborative robotics to mitigate the risks of human injury and environmental damage. Apart from collision avoidance, it is crucial for robots to rapidly detect and respond to unexpected collisions. While several learning-based collision detection methods have been introduced as alternatives to purely model-based detection techniques, there is currently a lack of such methods designed for collaborative robots equipped with variable stiffness actuators. Moreover, there is potential for further enhancing the network's robustness and improving the efficiency of data training. In this paper, we propose a new network, the Modularized Attention-Dilated Convolutional Neural Network (MAD-CNN), for collision detection in robots equipped with variable stiffness actuators. Our model incorporates a dual inductive bias mechanism and an attention module to enhance data efficiency and improve robustness. In particular, MAD-CNN is trained using only a four-minute collision dataset focusing on the highest level of joint stiffness. Despite limited training data, MAD-CNN robustly detects all collisions with minimal detection delay across various stiffness conditions. Moreover, it exhibits a higher level of collision sensitivity, which is beneficial for effectively handling false positives, which is a common issue in learning-based methods. Experimental results demonstrate that the proposed MAD-CNN model outperforms existing state-of-the-art models in terms of collision sensitivity and robustness.
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。
