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.

翻译：在协作机器人领域，确保安全性对于降低人体伤害和环境破坏风险至关重要。除避碰功能外，机器人还需具备快速检测与响应意外碰撞的能力。尽管基于学习的碰撞检测方法已作为纯模型检测技术的替代方案被提出，但目前尚缺乏专为配备变刚度执行器的协作机器人设计的此类方法。此外，网络鲁棒性与数据训练效率仍有提升空间。本文提出新型网络架构——模块化注意力膨胀卷积神经网络（MAD-CNN），用于配备变刚度执行器的机器人碰撞检测。该模型通过双重归纳偏置机制与注意力模块提升数据利用率与鲁棒性。特别地，MAD-CNN仅需基于最高关节刚度水平的四分钟碰撞数据集进行训练。在有限训练数据条件下，MAD-CNN仍能在各刚度条件下以极低检测延迟实现稳健的碰撞检测，并展现出更高的碰撞灵敏度——这一特性有助于有效处理学习型方法中常见的误报问题。实验结果表明，所提MAD-CNN模型在碰撞灵敏度与鲁棒性方面均优于现有最优模型。