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Article

Uncertainty-Aware Knowledge Distillation for Collision Identification of Collaborative Robots

1
Daegu-Gyeongbuk Research Center, Electronics and Telecommunications Research Institute (ETRI), Daegu 42994, Korea
2
Division of Electronic Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Anne Schmitz
Sensors 2021, 21(19), 6674; https://doi.org/10.3390/s21196674
Received: 22 August 2021 / Revised: 3 October 2021 / Accepted: 5 October 2021 / Published: 8 October 2021
(This article belongs to the Special Issue Human-Robot Collaborations in Industrial Automation)
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. View Full-Text
Keywords: collision identification; collaborative robot; deep learning; uncertainty estimation; knowledge distillation collision identification; collaborative robot; deep learning; uncertainty estimation; knowledge distillation
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MDPI and ACS Style

Kwon, W.; Jin, Y.; Lee, S.J. Uncertainty-Aware Knowledge Distillation for Collision Identification of Collaborative Robots. Sensors 2021, 21, 6674. https://doi.org/10.3390/s21196674

AMA Style

Kwon W, Jin Y, Lee SJ. Uncertainty-Aware Knowledge Distillation for Collision Identification of Collaborative Robots. Sensors. 2021; 21(19):6674. https://doi.org/10.3390/s21196674

Chicago/Turabian Style

Kwon, Wookyong, Yongsik Jin, and Sang J. Lee 2021. "Uncertainty-Aware Knowledge Distillation for Collision Identification of Collaborative Robots" Sensors 21, no. 19: 6674. https://doi.org/10.3390/s21196674

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