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From One to Many: How Active Robot Swarm Sizes Influence Human Cognitive Processes (2403.13541v1)

Published 20 Mar 2024 in cs.RO and cs.HC

Abstract: In robotics, understanding human interaction with autonomous systems is crucial for enhancing collaborative technologies. We focus on human-swarm interaction (HSI), exploring how differently sized groups of active robots affect operators' cognitive and perceptual reactions over different durations. We analyze the impact of different numbers of active robots within a 15-robot swarm on operators' time perception, emotional state, flow experience, and task difficulty perception. Our findings indicate that managing multiple active robots when compared to one active robot significantly alters time perception and flow experience, leading to a faster passage of time and increased flow. More active robots and extended durations cause increased emotional arousal and perceived task difficulty, highlighting the interaction between robot the number of active robots and human cognitive processes. These insights inform the creation of intuitive human-swarm interfaces and aid in developing swarm robotic systems aligned with human cognitive structures, enhancing human-robot collaboration.

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References (31)
  1. A. Kolling, P. Walker, N. Chakraborty, K. Sycara, and M. Lewis, “Human interaction with robot swarms: A survey,” IEEE Transactions on Human-Machine Systems, vol. 46, no. 1, pp. 9–26, 2015.
  2. M. Dorigo, V. Trianni, E. Şahin, R. Groß, T. H. Labella, G. Baldassarre, S. Nolfi, J.-L. Deneubourg, F. Mondada, D. Floreano et al., “Evolving self-organizing behaviors for a swarm-bot,” Autonomous Robots, vol. 17, no. 2, pp. 223–245, 2004.
  3. C. C. Ashcraft, M. A. Goodrich, and J. W. Crandall, “Moderating Operator Influence in Human-Swarm Systems,” 2019 IEEE Int. Conf. on Systems, Man and Cybernetics (SMC), vol. 00, pp. 4275–4282, 2019.
  4. S. Nagavalli, L. Luo, N. Chakraborty, and K. Sycara, “Neglect Benevolence in Human Control of Robotic Swarms,” 2014 IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 6047–6053, 2014.
  5. J. Kaduk, M. Cavdan, K. Drewing, A. Vatakis, and H. Hamann, “Effects of human-swarm interaction on subjective time perception: Swarm size and speed,” Proceedings of the 2023 ACM/IEEE Int. Conference on Human-Robot Interaction, pp. 456–465, 2023.
  6. A. Kolling, P. Walker, N. Chakraborty, K. Sycara, and M. Lewis, “Human interaction with robot swarms: A survey,” IEEE Transactions on Human-Machine Systems, vol. 46, no. 1, p. 9–26, 2016.
  7. M. Divband Soorati, J. Clark, J. Ghofrani, D. Tarapore, and S. D. Ramchurn, “Designing a user-centered interaction interface for human–swarm teaming,” Drones, vol. 5, no. 4, p. 131, 2021.
  8. A. G. Millard, R. Redpath, A. M. Jewers, C. Arndt, R. Joyce, J. A. Hilder, L. J. McDaid, and D. M. Halliday, “Ardebug: an augmented reality tool for analysing and debugging swarm robotic systems,” Frontiers in Robotics and AI, vol. 5, p. 87, 2018.
  9. I. Jang, J. Hu, F. Arvin, J. Carrasco, and B. Lennox, “Omnipotent virtual giant for remote human–swarm interaction,” in 2021 30th IEEE Int. Conference on Robot & Human Interactive Communication (RO-MAN).   IEEE, 2021, pp. 488–494.
  10. A. O. Abioye, M. Naiseh, W. Hunt, J. Clark, S. D. Ramchurn, and M. D. Soorati, “The effect of data visualisation quality and task density on human-swarm interaction,” in 2023 32nd IEEE Int. Conference on Robot and Human Interactive Communication (RO-MAN).   IEEE, 2023, pp. 1494–1501.
  11. A. Hussein, L. Ghignone, T. Nguyen, N. Salimi, H. Nguyen, M. Wang, and H. A. Abbass, “Towards bi-directional communication in human-swarm teaming: A survey,” arXiv, 2018.
  12. J. D. Hasbach and M. Bennewitz, “The design of self-organizing human–swarm intelligence,” Adaptive Behavior, vol. 30, no. 4, pp. 361–386, 2022.
  13. G. Podevijn, R. O’Grady, N. Mathews, A. Gilles, C. Fantini-Hauwel, and M. Dorigo, “Investigating the effect of increasing robot group sizes on the human psychophysiological state in the context of human–swarm interaction,” Swarm Intelligence, vol. 10, no. 3, pp. 193–210, 2016.
  14. G. Dietz, J. L. E, P. Washington, L. H. Kim, and S. Follmer, “Human Perception of Swarm Robot Motion,” Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 2520–2527, 2017.
  15. M. Wittmann, “Modulations of the experience of self and time,” Consciousness and Cognition, vol. 38, pp. 172–181, 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1053810015001452
  16. S. Sadeghi, R. Daziano, S.-Y. Yoon, and A. K. Anderson, “Affective experience in a virtual crowd regulates perceived travel time,” Virtual Reality, vol. 27, no. 2, pp. 1051–1061, 2023.
  17. E. A. Thomas and N. E. Cantor, “On the duality of simultaneous time and size perception,” Perception & Psychophysics, vol. 18, no. 1, pp. 44–48, 1975.
  18. V. Dormal, X. Seron, and M. Pesenti, “Numerosity-duration interference: A stroop experiment,” Acta psychologica, vol. 121, no. 2, pp. 109–124, 2006.
  19. S. W. Brown, “Time, change, and motion: The effects of stimulus movement on temporal perception,” Perception & psychophysics, vol. 57, pp. 105–116, 1995.
  20. M. Noulhiane, N. Mella, S. Samson, R. Ragot, and V. Pouthas, “How emotional auditory stimuli modulate time perception.” Emotion, vol. 7, no. 4, p. 697, 2007.
  21. A. Angrilli, P. Cherubini, A. Pavese, and S. Manfredini, “The influence of affective factors on time perception,” Perception & psychophysics, vol. 59, pp. 972–982, 1997.
  22. T. Matsuno and M. Tomonaga, “Visual search for moving and stationary items in chimpanzees (pan troglodytes) and humans (homo sapiens),” Behavioural Brain Research, vol. 172, no. 2, pp. 219–232, 2006.
  23. M. Wittmann, “The inner experience of time,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 364, no. 1525, p. 1955–1967, 2009.
  24. J. L. Plass and S. Kalyuga, “Four ways of considering emotion in cognitive load theory,” Educational Psychology Review, vol. 31, pp. 339–359, 2019.
  25. S.-h. Im and S. Varma, “Distorted time perception during flow as revealed by an attention-demanding cognitive task,” Creativity Research Journal, vol. 30, pp. 295–304, 07 2018.
  26. R. L. Piferi, K. A. Kline, J. Younger, and K. A. Lawler, “An alternative approach for achieving cardiovascular baseline: viewing an aquatic video,” Int. Journal of Psychophysiology, vol. 37, no. 2, p. 207–217, 2000.
  27. World Medical Association, “World medical association declaration of helsinki: ethical principles for medical research involving human subjects,” JAMA, vol. 310, no. 20, pp. 2191–2194, nov 2013.
  28. F. Riedo, M. Chevalier, S. Magnenat, and F. Mondada, “Thymio ii, a robot that grows wiser with children * *this work was supported by the swiss national center of the competence in research “robotics”,” 2013 IEEE Workshop on Advanced Robotics and its Social Impacts, p. 187–193, 2013.
  29. M. M. Bradley and P. J. Lang, “Measuring emotion: The self-assessment manikin and the semantic differential,” Journal of Behavior Therapy and Experimental Psychiatry, vol. 25, no. 1, p. 49–59, 1994.
  30. H. Lejeune and J. H. Wearden, “Vierordt’s the experimental study of the time sense (1868) and its legacy,” European Journal of Cognitive Psychology, vol. 21, no. 6, pp. 941–960, 2009.
  31. M. Cavdan, B. Celebi, and K. Drewing, “Simultaneous emotional stimuli prolong the timing of vibrotactile events,” IEEE Transactions on Haptics, pp. 622–627, 2023.
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Authors (4)
  1. Julian Kaduk (3 papers)
  2. Müge Cavdan (1 paper)
  3. Knut Drewing (3 papers)
  4. Heiko Hamann (31 papers)

Summary

  • The paper shows that increasing active robot numbers accelerates perceived time up to a threshold while enhancing flow experience.
  • The paper employs controlled experiments in a robot arena with pre- and post-interaction assessments to measure cognitive responses.
  • The paper reveals that larger active robot swarms heighten task difficulty and emotional arousal, emphasizing the need for adaptive interface designs.

Understanding the Impact of Swarm Size on Human Cognitive Processes in Robot Swarms

Introduction

Interest in how human-swarm interaction (HSI) affects human cognitive and perceptual reactions is increasing as swarm robotic systems become more prevalent. Human-swarm interaction differs significantly from traditional human-robot interaction by involving multiple autonomous robots that operate without central control, presenting unique challenges for human operators. This paper investigates the effects of varying active robot numbers within a swarm on aspects such as time perception, emotional state, flow experience, and perceived task difficulty. The findings shed light on how adjustments in the number of active robots influence human operators, with implications for designing more intuitive swarm interfaces and enhancing human-robot collaboration.

Experiment Design and Methodology

The researchers conducted experiments using a robot arena where participants aimed to prevent mobile robots from exiting. The robots executed random walk behaviors, with the human participant influencing their movements via a button interface. The primary variable was the number of active robots, varied across eight conditions within a total swarm size of 15. The experiment also considered the duration of interaction as a secondary variable.

Ethical approvals were secured, ensuring the paper conformed to the Declaration of Helsinki. Participants completed pre- and post-interaction questionnaires to measure their time estimation, emotional state, flow experience, and task difficulty perception.

Key Findings

The analysis revealed several important findings regarding human cognitive processing in HSI:

  • Time Perception: Increasing the number of active robots influenced participants' perception of time, making it appear to pass faster compared to when only a single robot was active. However, this effect plateaued in conditions with more than one active robot, indicating a threshold beyond which the number of active elements ceases to distort time perception significantly.
  • Flow Experience: Participants reported a higher flow state in conditions with multiple active robots compared to just one. This suggests that engaging with more active elements in a task can enhance the immersive experience of flow, where individuals feel fully involved in and enjoy the activities.
  • Task Difficulty and Emotional Arousal: Both perceived task difficulty and emotional arousal increased with the number of active robots and the duration of the interaction. These findings highlight a direct correlation between cognitive load and the complexity of the swarm interaction task.

Notably, the paper did not find a significant impact on emotional valence, suggesting that the number of active robots and interaction duration did not affect participants' overall emotional positivity or negativity.

Implications for Human-Swarm Interface Design

The insights from this paper have profound implications for designing interfaces and interaction strategies for swarm robotics. Particularly, the findings on time perception and flow experience suggest that optimally engaging operators may require careful management of active robot numbers to align with human cognitive capabilities. Additionally, the increase in task difficulty and arousal with larger active swarms points to the need for adaptive interfaces that can dynamically adjust the complexity of the interaction based on the operator's response.

Future Directions in AI and Robotics

This paper paves the way for further research into adaptive and personalized swarm behaviors that can improve human-robot collaboration. Future studies could explore how different interaction modalities (e.g., visual, auditory, or haptic feedback) impact human cognitive processes in HSI. Additionally, the development of predictive models to dynamically adjust swarm configurations based on real-time assessments of the human operator's cognitive state could enhance efficiency and satisfaction in human-swarm interactions.

In conclusion, understanding and harnessing the human cognitive processes impacted by swarm size and dynamics is crucial for advancing human-swarm interaction and creating more effective, user-friendly collaborative systems.