Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends

5 January 2021

Papers citing "Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends"

9 / 9 papers shown

Title
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies Chen Xu Tony Nguyen Emma Dixon Christopher Rodriguez Patrick "Tree" Miller Robert Lee Paarth Shah Rares Ambrus Haruki Nishimura Masha Itkina OffRL 78 2 0 11 Mar 2025
System Safety Monitoring of Learned Components Using Temporal Metric Forecasting Sepehr Sharifi Andrea Stocco Lionel C. Briand AI4TS 27 1 0 21 May 2024
Unifying Evaluation of Machine Learning Safety Monitors Joris Guérin Raul Sena Ferreira Kevin Delmas Jérémie Guiochet 30 10 0 31 Aug 2022
Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification Pasquale Antonante Heath Nilsen Luca Carlone 32 23 0 22 May 2022
Learning for Robot Decision Making under Distribution Shift: A Survey Abhishek Paudel OOD OffRL 20 5 0 14 Mar 2022
Monocular Depth Estimation Based On Deep Learning: An Overview Chaoqiang Zhao Qiyu Sun Chongzhen Zhang Yang Tang Feng Qian MDE 55 251 0 14 Mar 2020
Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems Feiyang Cai X. Koutsoukos OODD 109 68 0 28 Jan 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015