Daegu-Gyeongbuk Research Center, Electronics and Telecommunications Research Institute (ETRI), Daegu 42994, Korea.
Division of Electronic Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Korea.
Sensors (Basel). 2021 Oct 8;21(19):6674. doi: 10.3390/s21196674.
Human-robot interaction has received a lot of attention as collaborative robots became widely utilized in many industrial fields. Among techniques for human-robot interaction, collision identification is an indispensable element in collaborative robots to prevent fatal accidents. This paper proposes a deep learning method for identifying external collisions in 6-DoF articulated robots. The proposed method expands the idea of CollisionNet, which was previously proposed for collision detection, to identify the locations of external forces. The key contribution of this paper is uncertainty-aware knowledge distillation for improving the accuracy of a deep neural network. Sample-level uncertainties are estimated from a teacher network, and larger penalties are imposed for uncertain samples during the training of a student network. Experiments demonstrate that the proposed method is effective for improving the performance of collision identification.
人机交互受到了广泛关注，因为协作机器人在许多工业领域得到了广泛应用。在人机交互技术中，碰撞识别是协作机器人中防止致命事故的一个不可或缺的元素。本文提出了一种用于识别 6-DoF 关节机器人外部碰撞的深度学习方法。所提出的方法扩展了之前用于碰撞检测的 CollisionNet 的思想，以识别外力的位置。本文的主要贡献是不确定性感知知识蒸馏，用于提高深度神经网络的准确性。从教师网络中估计样本级别的不确定性，并在学生网络的训练过程中对不确定的样本施加更大的惩罚。实验表明，所提出的方法对于提高碰撞识别的性能是有效的。
