  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.
