journal article·2023-03-29Open access

Considerations for Developing Robot-Assisted Crisis De-Escalation Practices

Kathryn Pierce, Debra J. Pepler, Stephanie G. Craig, Michael Jenkin

Applied Sciences· Volume 13· Issue 7

Abstract

Robots are increasingly entering the social sphere and taking on more sophisticated roles. One application for which robots are already being deployed is in civilian security tasks, in which robots augment security and police forces. In this domain, robots will encounter individuals in crisis who may pose a threat to themselves, others, or personal property. In such interactions with human police and security officers, a key goal is to de-escalate the situation to resolve the interaction. This paper considers the task of utilizing mobile robots in de-escalation tasks, using the mechanisms developed for de-escalation in human–human interactions. What strategies should a robot follow in order to leverage existing de-escalation approaches? Given these strategies, what sensing and interaction capabilities should a robot be capable of in order to engage in de-escalation tasks with humans? First, we discuss the current understanding of de-escalation with individuals in crisis and present a working model of the de-escalation process and strategies. Next, we review the capabilities that an autonomous agent should demonstrate to be able to apply such strategies in robot-mediated crisis de-escalation. Finally, we explore data-driven approaches to training robots in de-escalation and the next steps in moving the field forward.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Anomaly Detection Techniques and Applications · Social Robot Interaction and HRI · Context-Aware Activity Recognition Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Psychology, York University, Toronto, ON M3J 1P3, Canada

Department of Psychology, University of Guelph, Guelph, ON N1G 2W1, Canada

Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada

Funding

Canadian Innovation for Defence Excellence and Security (IDEaS) Innovation Networks

References (64)

Show source-provided references
1. Rubenstein, M., Cimino, B., Nagpal, R., and Werfel, J. (2015, January 26–30). AERobot: An affordable one-robot-per-student system for early robotics education. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.10.1109/ICRA.2015.7140056 ↗
2. Robinson, N.L., Connolly, J., Suddery, G., Turner, M., and Kavanagh, D.J. (2021, January 8–12). A humanoid social robot to provide personalized feedback for health promotion in diet, physical activity, alcohol and cigarette use: A health clinic trial. Proceedings of the 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), Vancouver, BC, Canada.10.1109/RO-MAN50785.2021.9515558 ↗
3. A Long-Term Engagement with a Social Robot for Autism Therapy10.3389/frobt.2021.669972 ↗
4. Brumson, B. (2023, March 28). Robotics in Security and Military Applications. Available online: https://www.automate.org/industry-insights/robotics-in-security-and-military-applications.
5. (2023, March 28). Knightscope. Available online: https://www.knightscope.com.
6. Taylor, J.A., Murray, R., Shepler, L., and Davis, A. (2023, March 28). Mitigation of Occupational Violence to Firefighters and EMS Responders, Available online: https://www.usfa.fema.gov/downloads/pdf/publications/mitigation_of_occupational_violence.pdf.
7. Decompressing aggressive. inpatients: Breaking the aggression cycle to enhance positive outcome10.1002/bsl.2370060410 ↗
8. Roberts, A.R. (2005). Crisis Intervention Handbook: Assessment, Treatment, and Research, Oxford University Press. [3rd ed.].
9. National Institute for Clinical Excellence (2005). Violence: The Short Term Management of Disturbed/Violent Behaviour in Psychiatric In-Patient Settings and Emergency Departments National Cost-Impact Report, National Institute for Clinical Excellence.
10. Verbal De-escalation of the Agitated Patient: Consensus Statement of the American Association for Emergency Psychiatry Project BETA De-escalation Workgroup10.5811/westjem.2011.9.6864 ↗
11. A Systematic Social Observation Study of Police De-Escalation Tactics10.1177/1098611118784007 ↗
12. De-escalation: A survey of clinical staff in a secure mental health inpatient service10.1111/inm.12136 ↗
13. Effective ingredients of verbal de-escalation: Validating an English modified version of the ‘De-Escalating Aggressive Behaviour Scale’10.1111/jpm.12310 ↗
14. The De-Escalating Aggressive Behaviour Scale: Development and psychometric testing10.1111/j.1365-2648.2009.05087.x ↗
15. A survey of socially interactive robots10.1016/S0921-8890(02)00372-X ↗
16. Nocentini, O., Fiorini, L., Acerbi, G., Sorrentino, A., Mancioppi, G., and Cavallo, F. (2019). A Survey of Behavioral Models for Social Robots. Robotics, 8.10.20944/preprints201905.0251.v1 ↗
17. Does the chimpanzee have a theory of mind?10.1017/S0140525X00076512 ↗
18. Perner, J. (1991). Understanding the Representational Mind, MIT Press.
19. The aggression continuum: A paradigm shift
20. McKnight, S.E. (2020). De-Escalating Violence in Health Care: Strategies to Reduce Emotional Tension and Aggression, Sigma Theta Tau International.
21. Survival Skills for Working with Potentially Violent Clients10.1177/104438948306400603 ↗
22. The Overt Agitation Severity Scale for the objective rating of agitation10.1176/jnp.9.4.541 ↗
23. Profiles of Aggression among Psychiatric Patients10.1097/00005053-198809000-00007 ↗
24. Takayama, L., and Pantofaru, C. (2009, January 10–15). Influences on proxemic behaviors in human-robot interaction. Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA. Available online: https://dl.acm.org/doi/10.5555/1732643.1732940.10.1109/IROS.2009.5354145 ↗
25. Saskatchewan Association for Safe Workplaces in Health (2023, March 28). De-Escalation Verbal Crisis Intervention: Education Session Participant Handout. Available online: https://www.srsd119.ca/wp-content/uploads/SSS/SASW_De-escalation_Verbal-Crisis-Intervention_WEB.pdf.
26. Xiao, Z., Zhou, M.X., Chen, W., Yang, H., and Chi, C. (2020, January 25–30). If I hear you correctly: Building and evaluating interview chatbots with active listening skills. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA.10.1145/3313831.3376131 ↗
27. Bejarano, A., Lomax, O., Scherschel, P., and Williams, T. (2021). International Conference on Social Robotics, Springer.
28. The Interplay Between Emotional Intelligence, Trust, and Gender in Human–Robot Interaction10.1007/s12369-020-00624-1 ↗
29. Stereotypes or golden rules? Exploring likable voice traits of social robots as active aging companions for tech-savvy baby boomers in Taiwan10.1016/j.chb.2018.02.025 ↗
30. User—Robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy10.1007/s11370-008-0017-4 ↗
31. Kim, J., Kwak, S.S., and Kim, M. (October, January 27). Entertainment robot personality design based on basic factors of motions: A case study with ROLLY. Proceedings of the RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, Toyama, Japan.10.1109/ROMAN.2009.5326222 ↗
32. Social eye gaze in human-robot interaction: A review10.5898/JHRI.6.1.Admoni ↗
33. A Review of Eye Gaze in Virtual Agents, Social Robotics and HCI: Behaviour Generation, User Interaction and Perception10.1111/cgf.12603 ↗
34. Al Moubayed, S., Beskow, J., Skantze, G., and Granström, B. (2012). Cognitive Behavioural Systems, Springer.
35. Robot gaze does not reflexively cue human attention
36. Infants understand the referential nature of human gaze but not robot gaze10.1016/j.jecp.2013.02.007 ↗
37. Yoshikawa, Y., Shinozawa, K., Ishiguro, H., Hagita, N., and Miyamoto, T. (2006). Robotics: Science and Systems, Available online: http://www.roboticsproceedings.org/rss02/p37.pdf.
38. Yoshikawa, Y., Shinozawa, K., Ishiguro, H., Hagita, N., and Miyamoto, T. (2006, January 9–15). The effects of robot gaze on human attention and memory in a collaborative task. Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China.
39. Choi, J.J., Kim, Y., and Kwak, S.S. (2013, January 3–6). Have you ever Lied: The impacts of gaze avoidance on people’s perception of a robot. Proceedings of the 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Tokyo, Japan.10.1109/HRI.2013.6483523 ↗
40. Chidambaram, V., Chiang, Y.H., and Mutlu, B. (2012, January 5–8). Designing persuasive robots: How robots might persuade people using vocal and nonverbal cues. Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, Boston, MA, USA.10.1145/2157689.2157798 ↗
41. Combining Robotic Persuasive Strategies: The Persuasive Power of a Storytelling Robot that Uses Gazing and Gestures10.1007/s12369-015-0280-4 ↗
42. Is the Social Desirability Effect in Human–Robot Interaction overestimated? A Conceptual Replication Study Indicates Less Robust Effects10.1007/s12369-020-00688-z ↗
43. MacArthur, K.R., Stowers, K., and Hancock, P.A. (2017). Advances in Human Factors in Robots and Unmanned Systems, Springer.
44. Mumm, J., and Mutlu, B. (2011, January 6–9). Human-robot proxemics: Physical and psychological distancing in human-robot interaction. Proceedings of the 6th International Conference on Human-Robot Interaction, Lausanne, Switzerland.10.1145/1957656.1957786 ↗
45. From Proxemics Theory to Socially-Aware Navigation: A Survey10.1007/s12369-014-0251-1 ↗
46. The influence of biological sex, sexuality and gender role on interpersonal distance10.1348/014466605X58384 ↗
47. Human spatial behaviour
48. Psychological Effects of Behavior Patterns of a Mobile Personal Robot10.1023/A:1008986004181 ↗
49. Baghi, B.H., and Dudek, G. (2021). Sample efficient social navigation using inverse reinforcement learning. arXiv.
50. Chita-Tegmark, M., Lohani, M., and Scheutz, M. (2019, January 11–14). Gender effects in perceptions of robots and humans with varying emotional intelligence. Proceedings of the 2019 14th ACM/IEEE International Conference on Human–Robot Interaction (HRI), Daegu, Republic of Korea.10.1109/HRI.2019.8673222 ↗
51. Estimates of Emotional and Psychometric Intelligence: Evidence for Gender-Based Stereotypes10.3200/SOCP.144.2.149-162 ↗
52. Perceptions of gender differences in self-report measures of emotional intelligence10.1007/s11199-014-0368-6 ↗
53. Walters, M.L., Koay, K.L., Syrdal, D.S., Dautenhahn, K., and Te Boekhorst, R. (2023, March 28). Preferences and Perceptions of Robot Appearance and Embodiment in Human-Robot Interaction Trials. Proceedings of New Frontiers in Human-Robot Interaction. Available online: https://uhra.herts.ac.uk/bitstream/handle/2299/9642/903516.pdf?sequence=1&isAllowed=y.
54. De Silva, C.W. (2018). Intelligent Control: Fuzzy Logic Applications, CRC Press.10.1201/9780203750513 ↗
55. A few useful things to know about machine learning10.1145/2347736.2347755 ↗
56. A fast learning algorithm for deep belief nets10.1162/neco.2006.18.7.1527 ↗
57. Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press.
58. Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination10.1109/TCOMM.2019.2961332 ↗
59. Gao, Y., and Yu, N. (2021, January 16–18). Deep reinforcement learning in power distribution systems: Overview, challenges, and opportunities. Proceedings of the 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA.10.1109/ISGT49243.2021.9372283 ↗
60. Baghi, B.H., Konar, A., Hogan, F., Jenkin, M., and Dudek, G. (2022, January 23–27). SESNO: Sample Efficient Social Navigation from Observation. Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan.10.1109/IROS47612.2022.9981645 ↗
61. Police Use of Force Escalation and De-escalation: The Use of Systematic Social Observation With Video Footage10.1177/10986111211049145 ↗
62. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013). Playing Atari with deep reinforcement learning. arXiv.
63. Clark, J., and Amodei, D. (2023, March 28). Faulty Reward Functions in the Wild. Available online: https://openai.com/blog/faulty-reward-functions/.
64. Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams