Uncertainty-Aware Knowledge Distillation for Collision Identification of Collaborative Robots

Sensors

Yongsik Jin¹  Wookyong Kwon¹  Sang Jun Lee² 

[1] 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;

关键词: collision identification;    collaborative robot;    deep learning;    uncertainty estimation;    knowledge distillation;   

DOI  :  10.3390/s21196674

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown