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Failure Diagnosis

The following is a list of publications that are focused on robot failure diagnosis, namely the problem of analysing the causes of failures.

Scroll to the bottom of the page if you want to visualise a word cloud of the paper titles.

  1. M. Diehl and K. Ramirez-Amaro, “A causal-based approach to explain, predict and prevent failures in robotic tasks,” Robotics and Autonomous Systems (RAS), Elsevier, vol. 162, pp. 104376:1-12, Apr. 2023. Available: https://doi.org/10.1016/j.robot.2023.104376
  2. A. Mitrevski, P. G. Plöger, and G. Lakemeyer, “A Hybrid Skill Parameterisation Model Combining Symbolic and Subsymbolic Elements for Introspective Robots,” Robotics and Autonomous Systems, vol. 161, p. 104350:1-22, Mar. 2023. Available: https://doi.org/10.1016/j.robot.2022.104350
  3. L. Jahaj, S. M. Gutierrez, T. W. Rosmarin, F. Wotawa, and G. Steinbauer-Wagner, “A Model-based diagnosis integrated architecture for dependable autonomous robots,” in 34th International Workshop on Principles of Diagnosis (DX), 2023. Available: https://tugraz.elsevierpure.com/en/publications/a-model-based-diagnosis-integrated-architecture-for-dependable-au/
  4. A. Hasan, M. Tahavori, and H. S. Midtiby, “Model-Based Fault Diagnosis Algorithms for Robotic Systems,” IEEE Access, vol. 11, pp. 2250-2258, 2023. Available: https://doi.org/10.1109/ACCESS.2022.3233672
  5. M. Diehl and K. Ramirez-Amaro, “Why did I fail? A causal-based method to find explanations for robot failures,” IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 4, pp. 8925-8932, Oct. 2022. Available: https://doi.org/10.1109/LRA.2022.3188889
  6. A. Mitrevski, P. G. Plöger, and G. Lakemeyer, “Robot Action Diagnosis and Experience Correction by Falsifying Parameterised Execution Models,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 11025-11031. Available: https://doi.org/10.1109/ICRA48506.2021.9561710
  7. D. Habering, T. Hofmann, and G. Lakemeyer, “Using Platform Models for a Guided Explanatory Diagnosis Generation for Mobile Robots,” in Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021, pp. 1908-1914. Available: https://doi.org/10.24963/ijcai.2021/263
  8. C. Uhde, N. Berberich, K. Ramirez-Amaro and G. Cheng, “The Robot as Scientist: Using Mental Simulation to Test Causal Hypotheses Extracted from Human Activities in Virtual Reality,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 8081-8086. Available: https://doi.org/10.1109/IROS45743.2020.9341505
  9. A. Kuestenmacher and P. G. Plöger, “Model-Based Fault Diagnosis Techniques for Mobile Robots,” IFAC-PapersOnLine, vol. 49, no. 15, pp. 50-56, 2016. Available: https://doi.org/10.1016/j.ifacol.2016.07.613
  10. A. Kuestenmacher, N. Akhtar, P. G. Plöger, and G. Lakemeyer, “Towards Robust Task Execution for Domestic Service Robots,” Journal of Intelligent & Robotic Systems, vol. 76, no. 1, pp. 5-33, 2014. Available: https://doi.org/10.1007/s10846-013-0005-6
  11. S. Zaman, G. Steinbauer, J. Maurer, P. Lepej and S. Uran, “An integrated model-based diagnosis and repair architecture for ROS-based robot systems,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2013, pp. 482-489. Available: https://doi.org/10.1109/ICRA.2013.6630618
  12. R. Dearden and J. Ernits, “Automated Fault Diagnosis for an Autonomous Underwater Vehicle,” IEEE Journal of Oceanic Engineering, vol. 38, no. 3, pp. 484-499, July 2013. Available: https://doi.org/10.1109/JOE.2012.2227540
  13. V. Raman and H. Kress-Gazit, “Explaining Impossible High-Level Robot Behaviors,” IEEE Transactions on Robotics (T-RO), vol. 29, no. 1, pp. 94-104, Feb. 2013. Available: https://doi.org/10.1109/TRO.2012.2214558
  14. Q. Jiang, M. Jia, J. Hu, and F. Xu, “Machinery fault diagnosis using supervised manifold learning,” Mechanical Systems and Signal Processing, vol. 23, no. 7, pp. 2301-2311, Oct. 2009. Available: https://doi.org/10.1016/j.ymssp.2009.02.006
  15. L. E. Parker and B. Kannan, “Adaptive Causal Models for Fault Diagnosis and Recovery in Multi-Robot Teams,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2006, pp. 2703-2710. Available: https://doi.org/10.1109/IROS.2006.281993
  16. M. Brandstotter, M. W. Hofbaur, G. Steinbauer, and F. Wotawa, “Model-based fault diagnosis and reconfiguration of robot drives,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2007, pp. 1203-1209. Available: https://doi.org/10.1109/IROS.2007.4399092
  17. Y. L. Murphey, M. A. Masrur, and Z. Chen, “Fault Diagnostics in Electric Drives Using Machine Learning,” in Proceedings of Advances in Applied Artificial Intelligence (IEA/AIE), Lecture Notes in Computer Science, vol. 4031, 2006, pp. 1169-1178. Available: https://doi.org/10.1007/11779568_124
  18. M. Kalech, G. A. Kaminka, A. Meisels, and Y. Elmaliach, “Diagnosis of Multi-Robot Coordination Failures Using Distributed CSP Algorithms,” in Proceedings of the 21st AAAI Conference on Artificial Intelligence (AAAI), vol. 21, 2006, pp. 970-975. Available: https://aaai.org/papers/00970-aaai06-152-diagnosis-of-multi-robot-coordination-failures-using-distributed-csp-algorithms/
  19. H. Liu and G. M. Coghill, “A Model-Based Approach to Robot Fault Diagnosis”. in International Conference on Innovative Techniques and Applications of Artificial Intelligence, Applications and Innovations in Intelligent Systems XII (SGAI), 2005, pp. 137-150. Available: https://doi.org/10.1007/1-84628-103-2_10
  20. J. C. Bongard and H. Lipson, “Automated damage diagnosis and recovery for remote robotics,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2004, pp. 3545-3550, vol. 4. Available: https://doi.org/10.1109/ROBOT.2004.1308802
  21. A. L. Madsen, U. B. Kjaerulff, J. Kalwa, M. Perrier, and M. A. Sotelo, “Applications of Probabilistic Graphical Models to Diagnosis and Control of Autonomous Vehicles,” in 2nd Bayesian Modeling Applications Workshop, 2004. Available: https://vbn.aau.dk/en/publications/applications-of-probabilistic-graphical-models-to-diagnosis-and-c/
  22. R. Dearden, T. Willeke, R. Simmons, V. Verma, F. Hutter, and S. Thrun, “Real-time fault detection and situational awareness for rovers: report on the Mars technology program task,” in Proceedings of the IEEE Aerospace Conference, vol. 2, 2004, pp. 826-840. Available: https://doi.org/10.1109/AERO.2004.1367683
  23. M. Hashimoto, H. Kawashima, and F. Oba, “A multi-model based fault detection and diagnosis of internal sensors for mobile robot,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2003, pp. 3787-3792, vol. 3. Available: https://doi.org/10.1109/IROS.2003.1249744
  24. N. de Freitas, “Rao-Blackwellised particle filtering for fault diagnosis,” in Proceedings of the IEEE Aerospace Conference, 2002, pp. 1-6. Available: https://doi.org/10.1109/AERO.2002.1036890
  25. U. Lerner, R. Parr, D. Koller, and G. Biswas, Bayesian Fault Detection and Diagnosis in Dynamic Systems in Proceedings of 17th AAAI Conference on Artificial Intelligence, 2000, pp. 531-537. Available: https://aaai.org/papers/00531-aaai00-081-bayesian-fault-detection-and-diagnosis-in-dynamic-systems/
  26. J. M. Naughton, Y. C. Chen, and J. Jiang, “A neural network application to fault diagnosis for robotic manipulator,” in Proceeding of the IEEE International Conference on Control Applications, 1996, pp. 988-993. Available: https://doi.org/10.1109/CCA.1996.559050
  27. B. Freyermuth, “An approach to model based fault diagnosis of industrial robots,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 1991, pp. 1350-1356, vol.2. Available: https://doi.org/10.1109/ROBOT.1991.131801