awesome-robot-failure-management

Execution Monitoring / Anomaly and Failure Detection, and Failure Recovery

The following is a list of publications that deal with at least some aspect of anomaly / failure detection, overall execution monitoring, and / or failure recovery. The list also includes publications that specifically focus on learning-based methods for failure detection and / or recovery.

  1. C. Qi, X. Wang, S. Yong, S. Sheng, H. Mao, S. Srinivasan, M. Nambi, A. Zhang, and Y. Dattatreya, “Self-Refining Vision Language Model for Robotic Failure Detection and Reasoning,” in 14th International Conference on Learning Representations (ICLR), 2026. Available: https://openreview.net/forum?id=jr9hGWQioP
  2. H. Chong, J. Lee, and H. Ahn, “Robust Task Planning via Failure Detection Using Scene Graph From Multi-View Images,” IEEE Robotics and Automation Letters (RA-L), vol. 11, no. 2, pp. 1986-1993, Feb. 2026. Available: https://doi.org/10.1109/LRA.2025.3645659
  3. B. Santhanam, A. Mitrevski, S. Thoduka, S. Houben, and T. Hassan, “Reliable Robotic Task Execution in the Face of Anomalies,” IEEE Robotics and Automation Letters (RA-L), vol. 11, no. 1, pp. 314-321, Jan. 2026. Available: https://doi.org/10.1109/LRA.2025.3632090
  4. M. S. Sakib and Y. Sun, “STAR: A Foundation Model-Driven Framework for Robust Task Planning and Failure Recovery in Robotic Systems,” International Journal of Artificial Intelligence and Robotics Research, vol. 2, Jan. 2026. Available: https://doi.org/10.1142/S2972335325500073
  5. A. Kopken, N. Batti, A. S. Bauer, J. ButterfaB, T. Ehlert, and W. Friedl, “Toward Robust Task Execution through Telerobotic Failure Recovery in Space Operations,” in Proceedings of the IEEE Aerospace Conference, 2025, pp. 1-13. Available: https://doi.org/10.1109/AERO63441.2025.11068192
  6. C. Willibald, D. Sliwowski and D. Lee, “Multimodal Anomaly Detection with a Mixture-of-Experts,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025, pp. 20020-20027. Available: https://doi.org/10.1109/IROS60139.2025.11245878
  7. P. Vanc, G. Franzese, J. K. Behrens, C. D. Santina, K. Stepanova, and J. Kober, “ILeSiA: Interactive Learning of Robot Situational Awareness From Camera Input,” IEEE Robotics and Automation Letters (RA-L), vol. 10, no. 10, pp. 10490-10497, Oct. 2025. Available: https://doi.org/10.1109/LRA.2025.3601037
  8. Q. Gu, Y. Ju, S. Sun, I. Gilitschenski, H. Nishimura, M. Itkina, F. Shkurti, “SAFE: Multitask Failure Detection for Vision-Language-Action Models,” The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025. Available: https://openreview.net/forum?id=XPyAukgsFf
  9. A. Jamshidpey, M. Wahby, M. Allwright, W. Zhu, M. Dorigo, and M. K. Heinrich, “Centralization vs. decentralization in multi-robot sweep coverage with ground robots and UAVs,” Artificial Life and Robotics, Sept. 2025. Available: https://doi.org/10.1007/s10015-025-01049-7
  10. Y. Huang, N. Alvina, M. D. Shanthi, and T. Hermans, “Fail2Progress: Learning from Real-World Robot Failures with Stein Variational Inference,” Proceedings of The 9th Conference on Robot Learning (CoRL), 2025. Available: https://proceedings.mlr.press/v305/huang25d.html
  11. V. Nenchev and P. Sotiriadis, “Monitoring Progress and Failure in Autonomous Robot Navigation: A Case Study,” in International Conference on Runtime Verification (RV), Lecture Notes in Computer Science, vol. 16087, 2025, pp 317-335. Available: https://doi.org/10.1007/978-3-032-05435-7_18
  12. J. Duan, W. Pumacay, N. Kumar, Y. R. Wang, S. Tian, W. Yuan, R. Krishna, D. Fox, A. Mandlekar, and Y. Guo, “AHA: A Vision-Language-Model for Detecting and Reasoning over Failures in Robotic Manipulation,” in 13th International Conference on Learning Representations (ICLR), 2025. Available: https://openreview.net/forum?id=JVkdSi7Ekg
  13. S. Thoduka, S. Houben, J. Gall and P. G. Plöger, “Enhancing Video-Based Robot Failure Detection Using Task Knowledge,” in Proceedings of the European Conference on Mobile Robots (ECMR), 2025, pp. 1-6. Available: https://doi.org/10.1109/ECMR65884.2025.11162998
  14. F. Ahmad, H. Ismail, J. Styrud, M. Stenmark, and V. Krueger, “A Unified Framework for Real-Time Failure Handling in Robotics Using Vision-Language Models, Reactive Planner and Behavior Trees,” in Proceedings of the IEEE 21st International Conference on Automation Science and Engineering (CASE), 2025, pp. 887-894. Available: https://doi.org/10.1109/CASE58245.2025.11164021
  15. X. Xu, D. Bauer, and S. Song, “RoboPanoptes: The All-seeing Robot with Whole-body Dexterity,” in Proceedings of Robotics: Science and Systems (RSS), 2025. Available: https://doi.org/10.15607/RSS.2025.XXI.042
  16. C. Xu, T. K. Nguyen, E. Dixon, C. Rodriguez, P. Miller, R. Lee, P. Shah, R. A. Ambrus, H. Nishimura, and M. Itkina, “Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies” in Proceedings of Robotics: Science and Systems (RSS), 2025. Available: https://doi.org/10.15607/RSS.2025.XXI.073
  17. A. Gupta, Y. U. Ciftci, and S. Bansal, “Enhancing Robot Safety via MLLM-Based Semantic Interpretation of Failure Data,” in RSS Workshop on Robot Evaluation for the Real World, 2025. Available: https://openreview.net/forum?id=ltnb7YYCM7
  18. A. Tiwari, S. Kumar, R. K. Sharma, H. Mehdi, and M. Saroha, “Analysing the reliability factors of a robot utilized within an FMC comprising two machines and one robot,” International Journal on Interactive Design and Manufacturing (IJIDeM), vol. 19, pp. 4517-4531, June 2025. Available: https://doi.org/10.1007/s12008-025-02237-2
  19. S. Chen, C. Wang, K. Nguyen, L. Fei-Fei, and C. K. Liu, “ARCap: Collecting High-Quality Human Demonstrations for Robot Learning with Augmented Reality Feedback,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2025, pp. 8291-8298. Available: https://doi.org/10.1109/ICRA55743.2025.11128717
  20. J. Styrud, M. Iovino, M. Norrlöf, M. Björkman, and C. Smith, “Automatic Behavior Tree Expansion with LLMs for Robotic Manipulation,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2025, pp. 1225-1232. Available: https://doi.org/10.1109/ICRA55743.2025.11127942
  21. F. Ahmad, J. Styrud, and V. Krueger, “Addressing Failures in Robotics Using Vision-Based Language Models (VLMs) and Behavior Trees (BT),” in European Robotics Forum (ERF), Springer Proceedings in Advanced Robotics, vol. 36, 2025. Available: https://doi.org/10.1007/978-3-031-89471-8_43
  22. B. Cui, F. Huang, S. Li, and X. Yin, “Robust Temporal Logic Task Planning for Multirobot Systems Under Permanent Robot Failures,” IEEE Transactions on Control Systems Technology, vol. 33, no. 2, pp. 526-538, Mar. 2025. Available: https://doi.org/10.1109/TCST.2024.3494392
  23. C. Dawson, A. Parashar, and C. Fan, “RADIUM: Predicting and Repairing End-to-End Robot Failures Using Gradient-Accelerated Sampling,” IEEE Transactions on Robotics (T-RO), vol. 41, pp. 2268-2284, Mar. 2025. Available: https://doi.org/10.1109/TRO.2025.3551198
  24. D. Sliwowski and D. Lee, “ConditionNET: Learning Preconditions and Effects for Execution Monitoring,” IEEE Robotics and Automation Letters (RA-L), vol. 10, no. 2, pp. 1337-1344, Feb. 2025. Available: http://doi.org/10.1109/LRA.2024.3520916
  25. M. Wang, P. Zhang, G. Zhang, K. Sun, J. Zhang, and M. Jin, “A resilient scheduling framework for multi-robot multi-station welding flow shop scheduling against robot failures,” Robotics and Computer-Integrated Manufacturing, vol. 91, pp. 102835:1-16, Feb. 2025. Available: https://doi.org/10.1016/j.rcim.2024.102835
  26. K. Damak, M. Boujelbene, C. Acun, A. Alvanpour, S. K. Das, D. O. Popa, and O. Nasraoui, “Robot failure mode prediction with deep learning sequence models,” Neural Computing and Applications, vol. 37, pp. 4291-4302, Feb. 2025. Available: https://doi.org/10.1007/s00521-024-10856-1
  27. X. Yin, W. He, J. Wang, S. Peng, Y. Cao, and B. Zhang, “Health state assessment based on the Parallel–Serial Belief Rule Base for industrial robot systems,” Engineering Applications of Artificial Intelligence, vol. 142, pp. 109856:1-14, Feb. 2025. Available: https://doi.org/10.1016/j.engappai.2024.109856
  28. G. Boschetti and R. Minto, “A sensorless approach for cable failure detection and identification in cable-driven parallel robots,” Robotics and Autonomous Systems, vol. 183, pp. 104855:1-13, Jan. 2025. Available: https://doi.org/10.1016/j.robot.2024.104855
  29. K. S. Sangwan, A. Tusnial, and S. V. Iyer, “Stochastic robot failure management in an assembly line under industry 4.0 environment,” Production & Manufacturing Research, vol. 13, no. 1, pp. 2439275:1-19, Jan. 2025. Available: https://doi.org/10.1080/21693277.2024.2439275
  30. L. Klampfl and F. Wotawa, “Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems” in 35th International Conference on Principles of Diagnosis and Resilient Systems (DX), Open Access Series in Informatics (OASIcs), vol. 125, 2024, pp. 10:1-10:20. Available: https://doi.org/10.4230/OASIcs.DX.2024.10
  31. G. Steinbauer-Wagner, L. Fürbaß, M. De Bortoli, and L. Travé-Massuyès, “A Hierarchical Monitoring and Diagnosis System for Autonomous Robots” in 35th International Conference on Principles of Diagnosis and Resilient Systems (DX), Open Access Series in Informatics (OASIcs), vol. 125, 2024, pp. 1:1-1:9. Available: https://doi.org/10.4230/OASIcs.DX.2024.1
  32. F. Ahmad, M. Mayr, S. Suresh-Fazeela, and V. Krueger, “Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators (BTMG) Approach for Failure Management,” in Proceedings of the IEEE 20th International Conference on Automation Science and Engineering (CASE), 2024, pp. 1815-1822. Available: https://doi.org/10.1109/CASE59546.2024.10711715
  33. Y. Findik, H. Hasenfus and R. Azadeh, “Collaborative Adaptation for Recovery from Unforeseen Malfunctions in Discrete and Continuous MARL Domains,” in Proceedings of the IEEE 63rd Conference on Decision and Control (CDC), 2024, pp. 394-400. Available: https://doi.org/10.1109/CDC56724.2024.10885831
  34. S. Li, S. Zhang, G. He, and T. Jiang, “Discrete-Time Flocking Control in Multi-Robot Systems With Random Link Failures,” IEEE Transactions on Vehicular Technology, vol. 73, no. 9, pp. 12290-12304, Sept. 2024. Available: https://doi.org/10.1109/TVT.2024.3382617
  35. P. Kumar, I. Raouf, and H. S. Kim, “Transfer learning for servomotor bearing fault detection in the industrial robot,” Advances in Engineering Software, vol. 194, pp. 103672:1-10, Aug. 2024. Available: https://doi.org/10.1016/j.advengsoft.2024.103672
  36. S. Sagar, A. Taparia, and R. Senanayake, “Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models,” in Proceedings of the 41st International Conference on Machine Learning (ICML), 2024. Available: https://proceedings.mlr.press/v235/sagar24a.html
  37. C. Xiong, C. Shen, X. Li, K. Zhou, J. Liu, R. Wang, and H. Dong, “Autonomous Interactive Correction MLLM for Robust Robotic Manipulation,” in Proceedings of the 8th Conference on Robot Learning (CoRL), 2024. Available: https://proceedings.mlr.press/v270/xiong25a.html
  38. C. Agia, R. Sinha, J. Yang, Z. Cao, R. Antonova, M. Pavone, and J. Bohg, “Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress,” in Proceedings of the 8th Conference on Robot Learning (CoRL), 2024. Available: https://proceedings.mlr.press/v270/agia25a.html
  39. Z. Wang, B. Liang, V. Dhat, Z. Brumbaugh, N. Walker, R. Krishna, and M. Cakmak, “I Can Tell What I am Doing: Toward Real-World Natural Language Grounding of Robot Experiences,” in Proceedings of The 8th Conference on Robot Learning (CoRL), 2024. Available: https://proceedings.mlr.press/v270/wang25g.html
  40. M. Sanabria, I. Dusparic and N. Cardozo, “Learning Recovery Strategies for Dynamic Self-Healing in Reactive Systems,” in Proceedings of the IEEE/ACM 19th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 2024, pp. 133-142. Available: https://doi.org/10.1145/3643915.3644097
  41. S. Thoduka, N. Hochgeschwender, J. Gall, and P. G. Plöger, “A Multimodal Handover Failure Detection Dataset and Baselines,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2024, pp. 17013-17019. Available: https://doi.org/10.1109/ICRA57147.2024.10610143
  42. H. Liu, S. Dass, R. Martín-Martín and Y. Zhu, “Model-Based Runtime Monitoring with Interactive Imitation Learning,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2024, pp. 4154-4161. Available: https://doi.org/10.1109/ICRA57147.2024.10611038
  43. A. Inceoglu, E. E. Aksoy and S. Sariel, “Multimodal Detection and Classification of Robot Manipulation Failures,” IEEE Robotics and Automation Letters (RA-L), vol. 9, no. 2, pp. 1396-1403, Feb. 2024. Available: https://doi.org/10.1109/LRA.2023.3346270
  44. A. C. Ak, E. E. Aksoy and S. Sariel, “Learning Failure Prevention Skills for Safe Robot Manipulation,” IEEE Robotics and Automation Letters (RA-L), vol. 8, no. 12, pp. 7994-8001, Dec. 2023. Available: https://doi.org/10.1109/LRA.2023.3324587
  45. H. Dui, H. Xu, L. Zhang, and J. Wang, “Cost-based preventive maintenance of industrial robot system,” Reliability Engineering & System Safety, vol. 240, pp. 109595:1-11, Dec. 2023. Available: https://doi.org/10.1016/j.ress.2023.109595
  46. A. Elhafsi, R. Sinha, C. Agia, E. Schmerling, I. A. D. Nesnas, and M. Pavone “Semantic anomaly detection with large language models,” Autonomous Robots, vol. 47, pp. 1035-1055, Dec. 2023. Available: https://doi.org/10.1007/s10514-023-10132-6
  47. R. Thakker, M. Paton, M. P. Strub, M. Swan, G. Daddi, and R. Royce, “EELS: Towards Autonomous Mobility in Extreme Terrain with a Versatile Snake Robot with Resilience to Exteroception Failures,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, pp. 9886-9893. Available: https://doi.org/10.1109/IROS55552.2023.10341448
  48. I. Lee, H. J. Park, J.-W. Jang, C.-W. Kim, and J.-H. Choi, “System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes,” Applied Sciences, vol. 13, no. 18, pp. 10243:1-22, Sept. 2023. Available: https://doi.org/10.3390/app131810243
  49. Z. Liu, A. Bahety, and S. Song, “REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction,” in Proceedings of the 7th Conference on Robot Learning (CoRL), 2023. Available: https://proceedings.mlr.press/v229/liu23g.html
  50. T. Li, J. Zhang, S. Li, P. Zhou, and D. Lv “Neural-based adaptive fixed-time prescribed performance control for the flexible-joint robot with actuator failures,” Nonlinear Dynamics, vol. 111, pp. 16187-16214, Sept. 2023. Available: https://doi.org/10.1007/s11071-023-08714-1
  51. E. Sharma, C. Henke, A. Mitrevski, and P. G. Plöger, “Adaptive Compliant Robot Control with Failure Recovery for Object Press-Fitting,” in Proceedings of the European Conference on Mobile Robots (ECMR), 2023, pp. 1-7. Available: https://doi.org/10.1109/ECMR59166.2023.10256379
  52. S. Kalluraya, G. J. Pappas, and Y. Kantaros, “Resilient Temporal Logic Planning in the Presence of Robot Failures,” in Proceedings of the 62nd IEEE Conference on Decision and Control (CDC), 2023, pp. 7520-7526. Available: https://doi.org/10.1109/CDC49753.2023.10383968
  53. P. Gao, S. Siva, A. Micciche, and H. Zhang, “Collaborative Scheduling with Adaptation to Failure for Heterogeneous Robot Teams,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 1414-1420. Available: https://doi.org/10.1109/ICRA48891.2023.10161502
  54. E. Wescoat, S. Kerner, and L. Mears, “A comparative study of different algorithms using contrived failure data to detect robot anomalies,” in Proceedings of the 3rd International Conference on Industry 4.0 and Smart Manufacturing, Procedia Computer Science, vol. 200, pp. 669-678, 2022. Available: https://doi.org/10.1016/j.procs.2022.01.265
  55. T. Frasca and M. Scheutz, “A Framework for Robot Self-Assessment of Expected Task Performance,” IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 4, pp. 12523-12530, Oct. 2022. Available: https://doi.org/10.1109/LRA.2022.3219024
  56. A. Ramesh, R. Stolkin, and M. Chiou, “Robot Vitals and Robot Health: Towards Systematically Quantifying Runtime Performance Degradation in Robots Under Adverse Conditions,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10729-10736, Oct. 2022. Available: https://doi.org/10.1109/LRA.2022.3192612
  57. A. Reichlin, G. L. Marchetti, H. Yin, A. Ghadirzadeh, and D. Kragic, “Back to the Manifold: Recovering from Out-of-Distribution States,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 8660-8666. Available: https://doi.org/10.1109/IROS47612.2022.9981315
  58. G. Coruhlu, E. Erdem and V. Patoglu, “Explainable Robotic Plan Execution Monitoring Under Partial Observability,” IEEE Transactions on Robotics (T-RO), vol. 38, no. 4, pp. 2495-2515, Aug. 2022. Available: https://doi.org/10.1109/TRO.2021.3123840
  59. S. Diao, W. Sun, S. -F. Su, and J. Xia, “Adaptive Fuzzy Event-Triggered Control for Single-Link Flexible-Joint Robots With Actuator Failures,” IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7231-7241, Aug. 2022. Available: https://doi.org/10.1109/TCYB.2021.3049536
  60. A. Farid, D. Snyder, A. Z. Ren, and A. Majumdar, “Failure Prediction with Statistical Guarantees for Vision-Based Robot Control,” in Proceedings of Robotics: Science and Systems (RSS), 2022. Available: https://doi.org/10.15607/RSS.2022.XVIII.042
  61. B. A. Elsayed, T. Takemori, and F. Matsuno, “Joint failure recovery for snake robot locomotion using a shape-based approach,” Artificial Life and Robotics, vol. 27, pp. 341-354, May. 2022. Available: https://doi.org/10.1007/s10015-022-00742-1
  62. S. Mayya, R. K. Ramachandran, L. Zhou, V. Senthil, D. Thakur, and G. S. Sukhatme, “Adaptive and Risk-Aware Target Tracking for Robot Teams With Heterogeneous Sensors,” IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 2, pp. 5615-5622, Apr. 2022. Available: https://doi.org/10.1109/LRA.2022.3155805
  63. T. Ji, A. N. Sivakumar, G. Chowdhary, and K. Driggs-Campbell, “Proactive Anomaly Detection for Robot Navigation With Multi-Sensor Fusion,” IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 2, pp. 4975-4982, Apr. 2022. Available: https://doi.org/10.1109/LRA.2022.3153989
  64. U. Izagirre, I. Andonegui, I. Landa-Torres, and U. Zurutuza, “A practical and synchronized data acquisition network architecture for industrial robot predictive maintenance in manufacturing assembly lines,” Robotics and Computer-Integrated Manufacturing, vol. 74, pp. 102287, Apr. 2022. Available: https://doi.org/10.1016/j.rcim.2021.102287
  65. Y. J. Ng, M. S. K. Yeo, Q. B. Ng, M. Budig, M. A. V. J. Muthugala, S. M. B. P. Samarakoon, and R. E. Mohan, “Application of an adapted FMEA framework for robot-inclusivity of built environments,” Scientific Reports, vol. 12, pp. 3408:1-19, Mar. 2022. Available: https://doi.org/10.1038/s41598-022-06902-4
  66. T. Olsen, N. M. Stiffler, and J. M. O’Kane, “Rapid Recovery from Robot Failures in Multi-Robot Visibility-Based Pursuit-Evasion,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 9734-9741. Available: https://doi.org/10.1109/IROS51168.2021.9636141
  67. S. Mukherjee, C. Paxton, A. Mousavian, A. Fishman, M. Likhachev, and D. Fox, “Reactive Long Horizon Task Execution via Visual Skill and Precondition Models,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 5717-5724. Available: https://doi.org/10.1109/IROS51168.2021.9636037
  68. S. Thoduka, J. Gall and P. G. Plöger, “Using Visual Anomaly Detection for Task Execution Monitoring,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 4604-4610. Available: https://doi.org/10.1109/IROS51168.2021.9636133
  69. P. Aivaliotis, Z. Arkouli, K. Georgoulias, and S. Makris, “Degradation curves integration in physics-based models: Towards the predictive maintenance of industrial robots,” Robotics and Computer-Integrated Manufacturing, vol. 71, pp. 102177:1-17, Oct. 2021. Available: https://doi.org/10.1016/j.rcim.2021.102177
  70. B. Thananjeyan, A. Balakrishna, S. Nair, M. Luo, K. Srinivasan, M. Hwang, J. E. Gonzalez, J. Ibarz, C. Finn, and K. Goldberg, “Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones,” IEEE Robotics and Automation Letters (RA-L), vol. 6, no. 3, pp. 4915-4922, July 2021. Available: https://doi.org/10.1109/LRA.2021.3070252
  71. S. Thoduka and N. Hochgeschwender, “Benchmarking Robots by Inducing Failures in Competition Scenarios,” in Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. AI, Product and Service (HCII), 2021, pp. 263-276. Available: https://doi.org/10.1007/978-3-030-77820-0_20
  72. A. Raviola, A. De Martin, R. Guida, G. Jacazio, S. Mauro, and M. Sorli, “Harmonic Drive Gear Failures in Industrial Robots Applications: An Overview,” in Proceedings of the 6th European Conference of the Prognostics and Health Management Society, vol. 6, no. 1, pp. 350-360, 2021. Available: https://doi.org/10.36001/phme.2021.v6i1.2849
  73. C.-M. Hung, L. Sun, Y. Wu, I. Havoutis and I. Posner, “Introspective Visuomotor Control: Exploiting Uncertainty in Deep Visuomotor Control for Failure Recovery,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 6293-6299. Available: https://doi.org/10.1109/ICRA48506.2021.9561749
  74. P. Mitrano, D. McConachie, and D. Berenson, “Learning where to trust unreliable models in an unstructured world for deformable object manipulation,” Science Robotics, vol. 6, no. 54, p. eabd8170, May 2021. Available: https://doi.org/10.1126/scirobotics.abd8170
  75. A. Marco, D. Baumann, M. Khadiv, P. Hennig, L. Righetti, and S. Trimpe, “Robot Learning With Crash Constraints,” IEEE Robotics and Automation Letters (RA-L), vol. 6, no. 2, pp. 1439-1446, Apr. 2021. Available: https://doi.org/10.1109/LRA.2021.3057055
  76. T. Defard, A. Setkov, A. Loesch, and R. Audigier, “PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization,” in Proceedings of the International Conference on Pattern Recognition (ICPR), 2021, pp. 475-489. Available: https://doi.org/10.1007/978-3-030-68799-1_35
  77. A. Lagrassa, S. Lee and O. Kroemer, “Learning Skills to Patch Plans Based on Inaccurate Models,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 9441-9448. Available: https://doi.org/10.1109/IROS45743.2020.9341475
  78. J. Zhang and W. Song, “Physics-of-Failure based Model for Industrial Robot Reliability Prediction,” in Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA), 2020, pp. 729-734. Available: https://doi.org/10.1109/ICMA49215.2020.9233702
  79. B. Bai, Z. Li, Q. Wu, C. Zhou, and J. Zhang, “Fault data screening and failure rate prediction framework-based bathtub curve on industrial robots,” Industrial Robot, vol. 47, no. 6, pp. 867-880, Oct. 2020. Available: https://doi.org/10.1108/IR-02-2020-0031
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