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.

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

  1. Z. Hu, R. Wu, N. Enock, J. Li, R. Kadakia, and Z. Erickson, “RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction,” IEEE Transactions on Robotics (T-RO), vol. 42, pp. 2843-2860, June 2026. Available: https://doi.org/10.1109/TRO.2026.3706552
  2. X. Zeng, X. Zhou, Y. Li, J. Shi, T. Li, L. Chen, L. Ren, and Y. Li, “Diagnose, Correct, and Learn from Manipulation Failures via Visual Symbols,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 42386-42395. Available: https://openaccess.thecvf.com/content/CVPR2026/html/Zeng_Diagnose_Correct_and_Learn_from_Manipulation_Failures_via_Visual_Symbols_CVPR_2026_paper.html
  3. G. Zhao, Z. Tang, X. Chen, Z. Kuang, Y. Tian, and G. Li, “FLARE: A Failure-Aware Framework for Autonomous Correction and Recovery in Visual-Language Robotic Manipulation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 22391-22401. Available: https://openaccess.thecvf.com/content/CVPR2026/html/Zhao_FLARE_A_Failure-Aware_Framework_for_Autonomous_Correction_and_Recovery_in_CVPR_2026_paper.html
  4. H. Luo, X. Bu, H. Wang, S. Guo, J. Liu, and W. Li, “Failure Detection With Zero-Shot Error Correction in Robotic Manipulation,” IEEE Robotics and Automation Letters (RA-L), vol. 11, no. 5, pp. 6256-6263, May 2026. Available: https://doi.org/10.1109/LRA.2026.3678124
  5. C. Ling, L. Yang, J. Lei and S. J. Bae, “Reliability-Based Preventive Maintenance for a Humanoid Robot System Under Dependent Competing Failure Processes,” IEEE Transactions on Reliability, vol. 75, pp. 2037-2051, 2026. Available: https://doi.org/10.1109/TR.2026.3696061
  6. N. Grambow, L. Fenner, F. Kempkes, P. Hotz, D. Wan, J. Krüger, and K. Haninger, “Anomaly Detection for Generic Failure Monitoring in Robotic Assembly, Screwing and Manipulation,” in IEEE Robotics and Automation Letters (RA-L), vol. 11, no. 4, pp. 4737-4744, April 2026. Available: https://doi.org/10.1109/LRA.2026.3664647
  7. 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
  8. K. T. Ly, K. Lu, and I. Havoutis, “InteLiPlan: An Interactive Lightweight LLM-Based Planner for Domestic Robot Autonomy,” IEEE Robotics and Automation Letters (RA-L), vol. 11, no. 3, pp. 3875-3882, Mar. 2026. Available: https://doi.org/10.1109/LRA.2026.3662577
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. R. Römer, A. Kobras, L. Worbis, and A. Schoellig, “Failure Prediction at Runtime for Generative Robot Policies,” 38th Annual Conference on Neural Information Processing Systems (NeurIPS), 2025. Available: https://proceedings.neurips.cc/paper_files/paper/2025/hash/0b7cb3b8cc44e652761245537027db44-Abstract-Conference.html
  16. Q. Gu, Y. Ju, S. Sun, I. Gilitschenski, H. Nishimura, M. Itkina, and F. Shkurti, “SAFE: Multitask Failure Detection for Vision-Language-Action Models,” 38th Annual Conference on Neural Information Processing Systems (NeurIPS), 2025. Available: https://proceedings.neurips.cc/paper_files/paper/2025/hash/392d0d05e2f514063e6ce6f8b370834c-Abstract-Conference.html
  17. 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
  18. 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
  19. 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
  20. 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
  21. P. Pacaud, R. G. Pinel, S. Chen, C. Schmid, “Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models,”, Workshop on Making Sense of Data in Robotics: Composition, Curation, and Interpretability at Scale at CoRL 2025, 2025. Available: https://openreview.net/forum?id=wps46mtC9B
  22. 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
  23. 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
  24. E. Zhou, Q. Su, C. Chi, Z. Zhang, Z. Wang, T. Huang, L. Sheng, and H. Wang “Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 6919-6929. Available: https://doi.org/10.1109/CVPR52734.2025.00649
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53. J. Clark, K. Shah and M. Schwager, “Online Path Repair: Adapting to Robot Failures in Multi-Robot Aerial Surveys,” IEEE Robotics and Automation Letters (RA-L), vol. 9, no. 3, pp. 2319-2326, Mar. 2024. Available: https://doi.org/10.1109/LRA.2024.3355730
  54. 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
  55. 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
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66. 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
  67. 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
  68. P. Hegemann, T. Zechmeister, M. Grotz, K. Hitzler, and T. Asfour, “Learning Symbolic Failure Detection for Grasping and Mobile Manipulation Tasks,”, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 4302-4309. Available: https://doi.org/10.1109/IROS47612.2022.9982223
  69. 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
  70. 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
  71. 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
  72. 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
  73. A. Gautam, T. Whiting, X. Cao, M. A. Goodrich, and J. W. Crandall, “A Method for Designing Autonomous Robots that Know Their Limits,” in Proceedings of the International Conference on Robotics and Automation (ICRA), 2022, pp. 121-127. Available: https://doi.org/10.1109/ICRA46639.2022.9812030
  74. 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
  75. 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
  76. 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
  77. 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
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