A Comprehensive Narrative Review of Abrupt Movements in Human–Robot Interaction
Source abstract
Abstract
Human–robot interaction (HRI) takes place in dynamic environments where both humans and robots act as active agents, making the system inherently unpredictable. Abrupt movements can originate from either side and include human reflexes, fatigue, or unexpected reactions, as well as robot malfunctions, control errors, or task changes. These unpredictable events generate significant risks for both interaction fluency and safety, affecting not only the physical domain (e.g., collisions, excessive forces) but also cognitive aspects such as trust and predictability. Although different application areas present domain-specific challenges, a comprehensive overview of abrupt movements in HRI is still lacking, especially in the industrial scenario. This review aims to consolidate current knowledge regarding how abrupt phenomena are analyzed, prevented, and mitigated across various contexts and to offer new insights for researchers. In detail, after describing the literature search and the screening process, the review categorizes abrupt events, highlights key methodological approaches, and identifies gaps and future directions. By providing a structured synthesis of existing strategies, this work guides researchers in developing safer and more adaptive HRI frameworks capable of handling unpredictability.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Human-Automation Interaction and Safety · Social Robot Interaction and HRI · Robot Manipulation and Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy
References (83)
Show source-provided references
1. Detecting the Unexpected10.1016/j.conb.2015.08.003 ↗
2. Han, J., and Conti, D. (2025). Recent Advances in Human–Robot Interactions. Appl. Sci., 15.10.3390/app15126850 ↗
3. Multimodal Human–Robot Interaction for Human-Centric Smart Manufacturing: A Survey10.1002/aisy.202300359 ↗
4. Human–Robot Interaction: A Survey10.1561/1100000005 ↗
5. A Survey of Methods for Safe Human-Robot Interaction10.1561/2300000052 ↗
6. Dragan, A.D., Lee, K.C.T., and Srinivasa, S.S. (2013, January 3–6). Legibility and Predictability of Robot Motion. Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, Tokyo, Japan.10.1109/HRI.2013.6483603 ↗
7. Haddadin, S., and Croft, E. (2016). Physical Human–Robot Interaction. Springer Handbook of Robotics, Springer.10.1007/978-3-319-32552-1_69 ↗
8. A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction10.1177/0018720811417254 ↗
9. Improvement of Safety in Collaborative Robotics through Prompt Detection of Abrupt Movements
10. Digo, E., Polito, M., Caselli, E., Gastaldi, L., and Pastorelli, S. (2025). A Dataset of Standard and Abrupt Industrial Gestures Recorded Through MIMUs. Robotics, 14.10.3390/robotics14120176 ↗
11. (2016). Robots and Robotic Devices—Collaborative Robots (Standard No. ISO/TS 15066:2016).
12. Survey on Human–Robot Collaboration in Industrial Settings: Safety, Intuitive Interfaces and Applications10.1016/j.mechatronics.2018.02.009 ↗
13. A Review of Safety Methods for Human-Robot Collaboration and a Proposed Novel Approach
14. Peta, K., Wiśniewski, M., Kotarski, M., and Ciszak, O. (2025). Comparison of Single-Arm and Dual-Arm Collaborative Robots in Precision Assembly. Appl. Sci., 15.10.3390/app15062976 ↗
15. Medical Robotics—Regulatory, Ethical, and Legal Considerations for Increasing Levels of Autonomy10.1126/scirobotics.aam8638 ↗
16. Robotics In Vivo: A Perspective on Human-Robot Interaction in Surgical Robotics10.1146/annurev-control-091219-013437 ↗
17. A Review on the Form and Complexity of Human–Robot Interaction in the Evolution of Autonomous Surgery10.1002/aisy.202400197 ↗
18. Gonzalez-Aguirre, J.A., Osorio-Oliveros, R., Rodríguez-Hernández, K.L., Lizárraga-Iturralde, J., Menendez, R.M., Ramírez-Mendoza, R.A., Ramírez-Moreno, M.A., and Lozoya-Santos, J.d.J. (2021). Service Robots: Trends and Technology. Appl. Sci., 11.10.3390/app112210702 ↗
19. Yao, J., He, C., Li, K., Su, R., and Ling, K.V. (2024, January 12–15). Hybridizing Long Short-Term Memory Network and Inverse Kinematics for Human Manipulation Prediction in Smart Manufacturing. Proceedings of the 2024 18th International Conference on Control, Automation, Robotics and Vision, ICARCV 2024, Dubai, United Arab Emirates.10.1109/ICARCV63323.2024.10821563 ↗
20. MIntNet: Rapid Motion Intention Forecasting of Coupled Human-Robot Systems With Simulation-to-Real Autoregressive Neural Networks10.1109/LRA.2023.3306646 ↗
21. Feleke, A.G., Bi, L., and Fei, W. (2021). Emg-Based 3d Hand Motor Intention Prediction for Information Transfer from Human to Robot. Sensors, 21.10.3390/s21041316 ↗
22. Variable Admittance Control With Human Intention Estimation for Physical Human–Robot Interaction10.1109/TIE.2025.3629315 ↗
23. Real-Time Trajectory Planning Based on Joint-Decoupled Optimization in Human-Robot Interaction10.1016/j.mechmachtheory.2019.103664 ↗
24. Tele-Aiming Control Design for Reconnaissance Robot Using a Strong Tracking Multi-Model Extended Super-Twisting Observer10.1049/cth2.12391 ↗
25. Dalvi, M., Chiddarwar, S.S., Rahul, M.R., and Sahoo, S.R. (2022). Kinematic Modelling of UR5 Cobot Using Dual Quaternion Approach. Machines, Mechanism and Robotics, Springer.10.1007/978-981-16-0550-5_103 ↗
26. Dalvi, M., Chiddarwar, S.S., Sahoo, S.R., and Rahul, M.R. (2021). Dual Quaternion-Based Kinematic Modelling of Serial Manipulators. Machines, Mechanism and Robotics, Springer.10.1007/978-981-15-3639-7_1 ↗
27. CPL-SLAM: Centralized Collaborative Multirobot Visual-Inertial SLAM Using Point-and-Line Features10.1109/JIOT.2025.3548958 ↗
28. Hybrid Tracking Module for Real-Time Tool Tracking for an Autonomous Exoscope10.1109/LRA.2024.3400124 ↗
29. Fransen, B.R., Herbst, E.V., Harrison, A.M., Adams, W., and Trafton, J.G. (2009, January 11–15). Real-Time Face and Object Tracking. Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, St. Louis, MO, USA.10.1109/IROS.2009.5354143 ↗
30. Zelinsky, A., and Heinzmann, J. (1996, January 11–14). Human-Robot Interaction Using Facial Gesture Recognition. Proceedings of the Robot and Human Communication—Proceedings of the IEEE International Workshop, Tsukuba, Japan.
31. On the Perceptual Advantages of Visual Suppression Mechanisms for Dynamic Robot Systems10.1016/j.procs.2016.07.472 ↗
32. Carfagni, M., Furferi, R., Governi, L., Santarelli, C., Servi, M., Uccheddu, F., and Volpe, Y. (2019). Metrological and Critical Characterization of the Intel D415 Stereo Depth Camera. Sensors, 19.10.3390/s19030489 ↗
33. Kastner, L., Fatloun, B., Shen, Z., Gawrisch, D., and Lambrecht, J. (2022, January 23–27). Human-Following and -Guiding in Crowded Environments Using Semantic Deep-Reinforcement-Learning for Mobile Service Robots. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.10.1109/ICRA46639.2022.9812111 ↗
34. Adachi, M., Kakio, M., Shiomi, M., and Miyashita, T. (2025, January 25–29). Virtual Robot Riding with a Person in a Real Elevator—Mixed Reality System for Robot Behavior Design. Proceedings of the IEEE International Workshop on Robot and Human Communication, RO-MAN, Eindhoven, The Netherlands.10.1109/RO-MAN63969.2025.11217868 ↗
35. Quintana, J.J., Ferrer, M.A., Diaz, M., Feo, J.J., Wolniakowski, A., and Miatluk, K. (2022). Uniform vs. Lognormal Kinematics in Robots: Perceptual Preferences for Robotic Movements. Appl. Sci., 12.10.3390/app122312045 ↗
36. Schorsch, J.F., Keemink, A.Q.L., Stienen, A.H.A., Van Der Helm, F.C.T., and Abbink, D.A. (2014, January 12–15). The Influence of Human-Robot Interaction Order during Fast Lifting Tasks for Different Levels of Weight Compensation. Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, Sao Paulo, Brazil.10.1109/BIOROB.2014.6913814 ↗
37. Ralph, M.B., and Moussa, M.A. (2006, January 2–3). On the Effect of the User’s Background on Communicating Grasping Commands. Proceedings of the HRI 2006: Proceedings of the 2006 ACM Conference on Human-Robot Interaction, Salt Lake City, UT, USA.
38. (2023, January 22–24). Hand Gesture Controlled Video Player Application. Proceedings of the 7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023—Proceedings, Coimbatore, India.
39. Jochum, E.A., and Derks, J. (2019). Tonight We Improvise!: Real-Time Tracking for Human-Robot Improvisational Dance, Association for Computing Machinery. ACM International Conference Proceeding Series.10.1145/3347122.3347129 ↗
40. Theofanidis, M., Lioulemes, A., and Makedon, F.S. (2016). A Motion and Force Analysis System for Human Upper-Limb Exercises, Association for Computing Machinery. ACM International Conference Proceeding Series.10.1145/2910674.2910698 ↗
41. Guda, V.K., Mugisha, S., Chevallereau, C., and Chablat, D. (2023). Introduction of a Cobot as Intermittent Haptic Contact Interfaces in Virtual Reality, Springer. Lecture Notes in Computer Science.10.1007/978-3-031-35741-1_36 ↗
42. He, A., Herron, C.W., Kalita, B., and Leonessa, A. (November, January 30). In-House Built Robust and Adaptable System Architecture for Virtual Reality Haptic Interface. Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), Columbus, OH, USA.
43. (2021). Robotics—Vocabulary (Standard No. ISO 8373:2021).
44. Jiang, X., Zhao, S., Zhu, Y., Li, Q., and Zhang, J. (2025). A Two-Stage Reinforcement Learning Framework for Humanoid Robot Sitting and Standing-Up. Biomimetics, 10.10.3390/biomimetics10110783 ↗
45. Physiology-Inspired EEG Transformer for Predicting Movement Transitions in Bimanual Tasks
46. Language-Driven Closed-Loop Grasping with Model-Predictive Trajectory Optimization10.1016/j.mechatronics.2025.103335 ↗
47. Safety in Wearable Robotic Exoskeletons: Design, Control, and Testing Guidelines10.1115/1.4066900 ↗
48. Adaptive Fuzzy Control of Time-Varying Impedance in Rehabilitation Exercises10.1177/01423312241260917 ↗
49. Kim, S., Jang, H., Ha, J., Lee, D., Ha, Y., and Song, Y. (2025). Time-Interval-Based Collision Detection for 4WIS Mobile Robots in Human-Shared Indoor Environments. Sensors, 25.10.3390/s25030890 ↗
50. Li, P., Feng, S., and Yu, H. (2025). Research on Target Localization and Adaptive Scrubbing of Intelligent Bathing Assistance System. Front. Bioeng. Biotechnol., 13.10.3389/fbioe.2025.1550875 ↗
51. Maehigashi, A., Kubo, K., Nungduk, Y., and Yamada, S. (2025, January 4–6). Effects of Robot Bowing during Apology on Trust Repair. Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, Melbourne, Australia.10.1109/HRI61500.2025.10973849 ↗
52. Optimized TD3 Algorithm for Robust Autonomous Navigation in Crowded and Dynamic Human-Interaction Environments10.1016/j.rineng.2024.102874 ↗
53. Factors Affecting Workers’ Mental Stress in Handover Activities During Human–Robot Collaboration10.1177/00187208241226823 ↗
54. Detection of Upper Limb Abrupt Gestures for Human–Machine Interaction Using Deep Learning Techniques10.1007/s40430-024-04746-9 ↗
55. Sun, D., Wang, J., Xu, Z., Bao, J., and Lu, H. (2024). Research on Human-Robot Collaboration Method for Parallel Robots Oriented to Segment Docking. Sensors, 24.10.21203/rs.3.rs-3830959/v1 ↗
56. Hariharasudhan, A., Tang, G., and Webb, P. (2024, January 11–13). The Development of an Augmented Reality Gesture Control Human-Robot Interface. Proceedings of the 2024 12th International Conference on Control, Mechatronics and Automation, ICCMA 2024, London, UK.10.1109/ICCMA63715.2024.10843879 ↗
57. Digo, E., Caselli, E., Polito, M., Antonelli, M., Gastaldi, L., and Pastorelli, S. (2023). Test–Retest Repeatability of Human Gestures in Manipulation Tasks. Appl. Sci., 13.10.20944/preprints202306.1383.v1 ↗
58. Hannum, C., Li, R., and Wang, W. (2023). A Trust-Assist Framework for Human–Robot Co-Carry Tasks. Robotics, 12.10.3390/robotics12020030 ↗
59. Renz, H., Krämer, M., and Bertram, T. (2023, January 4–9). Uncertainty Estimation for Predictive Collision Avoidance in Human-Robot Collaboration. Proceedings of the 2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023, Koh Samui, Thailand.10.1109/ROBIO58561.2023.10354561 ↗
60. Abrupt Movements Assessment of Human Arms Based on Recurrent Neural Networks for Interaction with Machines10.1007/978-3-031-45705-0_15 ↗
61. Deep Learning Technique to Identify Abrupt Movements in Human-Robot Collaboration10.1007/978-3-031-32439-0_9 ↗
62. Simas, H., and Di Gregorio, R. (2022). Collision Avoidance for Redundant 7-DOF Robots Using a Critically Damped Dynamic Approach. Robotics, 11.10.3390/robotics11050093 ↗
63. Detecting Impulsive Movements to Increase Operators’ Safety in Manufacturing10.1007/978-3-030-87383-7_19 ↗
64. Robot Apology as a Post-Accident Trust-Recovery Control Strategy in Industrial Human-Robot Interaction10.1016/j.ergon.2020.103078 ↗
65. Hannum, C., Li, R., and Wang, W. (2020, January 10–13). Trust or Not?: A Computational Robot-Trusting-Human Model for Human-Robot Collaborative Tasks. Proceedings of the 2020 IEEE International Conference on Big Data, Big Data 2020, Atlanta, GA, USA.10.1109/BigData50022.2020.9378119 ↗
66. Towards Skill Transfer via Learning-Based Guidance in Human-Robot Interaction: An Application to Orthopaedic Surgical Drilling Skill10.1007/s10846-019-01082-2 ↗
67. Zardykhan, D., Svarny, P., Hoffmann, M., Shahriari, E., and Haddadin, S. (2019, January 15–17). Collision Preventing Phase-Progress Control for Velocity Adaptation in Human-Robot Collaboration. Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Toronto, ON, Canada.10.1109/Humanoids43949.2019.9035065 ↗
68. Esmaeili, B., Beyramzad, J., Seyyedrasuli, M., Noorani, M.R.S., and Ghanbari, A. (2018, January 5–8). Using Fuzzy Neural Network Sliding Mode Control for Human-Exoskeleton Interaction Forces Minimization. Proceedings of the 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018, Changchun, China.10.1109/ICMA.2018.8484461 ↗
69. Stark, C., Pereira, A., and Althoff, M. (February, January 31). Reachset Conformance Testing of Human Arms with a Biomechanical Model. Proceedings of the 2nd IEEE International Conference on Robotic Computing, IRC 2018, Laguna Hills, CA, USA.10.1109/IRC.2018.00045 ↗
70. Gesture-Based Adaptive Haptic Guidance: A Comparison of Discriminative and Generative Modeling Approaches10.1109/LRA.2017.2660071 ↗
71. Quesque, F., Lewkowicz, D., Delevoye-Turrell, Y.N., and Coello, Y. (2013). Effects of Social Intention on Movement Kinematics in Cooperative Actions. Front. Neurorobot., 7.10.3389/fnbot.2013.00014 ↗
72. Assisting Manual Welding with Robot10.1016/j.rcim.2011.01.003 ↗
73. Méndez-Polanco, J.A., Muñoz-Meléndez, A., and Morales-Manzanares, E.F. (2010). Detection of Multiple People by a Mobile Robot in Dynamic Indoor Environments, Springer. Lecture Notes in Computer Science.10.1007/978-3-642-16952-6_53 ↗
74. Méndez-Polanco, J.A., Muñoz-Meléndez, A., and Morales, E.F. (2009). People Detection by a Mobile Robot Using Stereo Vision in Dynamic Indoor Environments, Springer. Lecture Notes in Computer Science.10.1007/978-3-642-05258-3_31 ↗
75. Exoskeletons With Virtual Reality, Augmented Reality, and Gamification for Stroke Patients’ Rehabilitation: Systematic Review10.2196/12010 ↗
76. Exoskeletons for Industrial Application and Their Potential Effects on Physical Work Load10.1080/00140139.2015.1081988 ↗
77. Exploring the Synergies between Collaborative Robotics, Digital Twins, Augmentation, and Industry 5.0 for Smart Manufacturing: A State-of-the-Art Review10.1016/j.rcim.2024.102769 ↗
78. Publications Office of the European Union (2021). Industry 5.0: Towards a Sustainable, Human-Centric and Resilient European Industry, Publications Office of the European Union.
79. Industry 4.0 and Industry 5.0—Inception, Conception and Perception10.1016/j.jmsy.2021.10.006 ↗
80. Raviola, A., Guida, R., De Martin, A., Pastorelli, S., Mauro, S., and Sorli, M. (2021). Effects of Temperature and Mounting Configuration on the Dynamic Parameters Identification of Industrial Robots. Robotics, 10.10.3390/robotics10030083 ↗
81. Hopko, S., Wang, J., and Mehta, R. (2022). Human Factors Considerations and Metrics in Shared Space Human-Robot Collaboration: A Systematic Review. Front. Robot. AI, 9.10.3389/frobt.2022.799522 ↗
82. Le, T.T.H., Prihatno, A.T., Oktian, Y.E., Kang, H., and Kim, H. (2023). Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review. Appl. Sci., 13.10.3390/app13095809 ↗
83. (2025). Robots and Robotic Devices—Safety Requirements for Industrial Robots—Part 2: Robot Systems and Integration (Standard No. ISO 10218-2:2025).