Advertisement

Autonomous Robots

, Volume 39, Issue 3, pp 293–312 | Cite as

Decision-making authority, team efficiency and human worker satisfaction in mixed human–robot teams

  • Matthew C. GombolayEmail author
  • Reymundo A. Gutierrez
  • Shanelle G. Clarke
  • Giancarlo F. Sturla
  • Julie A. Shah
Article

Abstract

In manufacturing, advanced robotic technology has opened up the possibility of integrating highly autonomous mobile robots into human teams. However, with this capability comes the issue of how to maximize both team efficiency and the desire of human team members to work with these robotic counterparts. To address this concern, we conducted a set of experiments studying the effects of shared decision-making authority in human–robot and human-only teams. We found that an autonomous robot can outperform a human worker in the execution of part or all of the process of task allocation (\(p<0.001\) for both), and that people preferred to cede their control authority to the robot \((p<0.001)\). We also established that people value human teammates more than robotic teammates; however, providing robots authority over team coordination more strongly improved the perceived value of these agents than giving similar authority to another human teammate \((p< 0.001)\). In post hoc analysis, we found that people were more likely to assign a disproportionate amount of the work to themselves when working with a robot \((p<0.01)\) rather than human teammates only. Based upon our findings, we provide design guidance for roboticists and industry practitioners to design robotic assistants for better integration into the human workplace.

Keywords

Human–robot teaming Planning and scheduling Team performance Human–robot interaction 

References

  1. Adams, J. A. (2009). Multiple robot-single human interaction: Effects on perceived workload and performance. Behavior and Information Technology, 28(2), 183–298.CrossRefGoogle Scholar
  2. Alsever, J. (2011). Robot workers take over warehouses. CNN Money. Retrieved November 9, 2011 from http://money.cnn.com/2011/11/09/smallbusiness/kiva_robots/.
  3. Ardissono, L., Petrone, G., Torta, G., & Segnan, M. (2012). Mixed-initiative scheduling of tasks in user collaboration. In Proceedings of WEBIST 2012—eight international conference on web information systems and technologies (pp. 342–351).Google Scholar
  4. Barnes, M. J., Chen, J. Y. C., Jentsch, F., & Redden, E. S., (2011). Designing effective soldier–robot teams in complex environments: Training, interfaces, and individual differences. In Proceedinggs of the international conference on engineering psychology and cognitive ergonomics (EPCE) (pp. 484–493). Berlin: Springer.Google Scholar
  5. Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (2010). Linear programming and network flows (3rd ed.). Hoboken: Wiley.zbMATHGoogle Scholar
  6. Berry, P., Peintner, B., Conley, K., Gervasio, M., Uribe, T., & Yorke-Smith, N. (2006). Deploying a personalized time management agent. Proceedings of the fifth international joint conference on autonomous agents and multiagent systems, AAMAS ’06 (pp. 1564–1571). New York: ACM.Google Scholar
  7. Bertsimas, Dimitris, & Weismantel, Robert. (2005). Optimization over Integers. Belmont: Dynamic Ideas.Google Scholar
  8. Blickensderfer, E., Cannon-Bowers, J. A., & Salas, E. (1998). Cross-training and team performance. making decisions under stress: Implications for individual and team training (pp. 299–311). Washington DC: American Psychological Association.CrossRefGoogle Scholar
  9. Casper, J., & Roberson, R. R. (2004). Human-robot interaction in rescue robotics. IEEE Transaction on Systems, Man, and Cybernetics (SMCS), 34(2), 138–153.CrossRefGoogle Scholar
  10. Chen, J. Y. C., Barnes, M. J., & Qu, Z. (2010). Roboleader: An agent for supervisory control of mobile robots. In Proceedings of the international conference on human-robot interaction (HRI).Google Scholar
  11. Clare, A. S., Cummings, M. L., How, J. P., Whitten, A. K., & Toupet, O. (2012). Operator objective function guidance for a real-time unmanned vehicle scheduling algorithm. Journal of Aerospace Computing, Information, and Communication, 9(4), 161–173.CrossRefGoogle Scholar
  12. Cummings, M. L., Brzezinski, A. S., & Lee, J. D. (2007). Operator performance and intelligent aiding in unmanned aerial vehicle scheduling. IEEE Intelligent Systems, 22(2), 52–59.CrossRefGoogle Scholar
  13. Durfee, E. H., Boerkoel, J. C, Jr, & Sleight, J. (2013). Using hybrid scheduling for the semi-autonomous formation of expert teams. Future Generation Computer Systems, 31, 200–212.CrossRefGoogle Scholar
  14. Entin, E. E., & Serfaty, D. (1999). Adaptive team coordination. Human Factors, 41, 312–325.CrossRefGoogle Scholar
  15. Feng, S., Whitman, E., Xinjilefu, X., & Atkeson, C. G. (2015). Optimization-based full body control for the darpa robotics challenge. Journal of Field Robotics, 32(2), 293–312.CrossRefGoogle Scholar
  16. Fox, D. (2003). Adapting the sample size in particle filters through kld-sampling. International Journal of Robotics Research (IJRR), 22, 985–1003.CrossRefGoogle Scholar
  17. Gombolay, M. C., Wilcox, R. J., & Shah, J. A. (2013). Fast scheduling of multi-robot teams with temporospatial constrints. In Proceedings of the robots: Science and systems (RSS) (pp. 24–28). Berlin.Google Scholar
  18. Goodrich, M. A., Morse, B. S., Engh, C., Cooper, J. L., & Adams, J. A. (2009). Towards using UAVs in wilderness search and rescue: Lessons from field trials. Interaction Studies, Special Issue on Robots in the Wild: Exploring Human-Robot Interaction in Naturalistic Environments, 10(3), 453–478.Google Scholar
  19. Hamasaki, M., Takeda, H., Ohmukai, I., & Ichise, R. (2004). Scheduling support system for academic conferences based on interpersonal networks. In Proceedings of ACM Hypertext.Google Scholar
  20. Haynes, T., Sen, S., Arora, N., & Nadella, R. (1997). An automated meeting scheduling system that utilizes user preferences. In Proceedings of the first international conference on autonomous agents, AGENTS ’97 (pp. 308–315). New York: ACM.Google Scholar
  21. Hebert, P., Bajracharya, M., Ma, J., Hudson, N., Aydemir, A., Reid, J., et al. (2015). Mobile manipulation and mobility as manipulationdesign and algorithms of robosimian. Journal of Field Robotics, 32(2), 255–274.CrossRefGoogle Scholar
  22. Hoffman, G. (2013). Evaluating fluency in human-robot collaboration. In International conference on human-robot interaction (HRI), workshop on human robot collaboration.Google Scholar
  23. Hoffman, G., & Breazeal, C. (2007). Effects of anticipatory action on human-robot teamwork: Efficiency, fluency, and perception of team. In Proceedings of the international conference on human-robot interaction (HRI) (pp. 1–8).Google Scholar
  24. Hooten, E. R., Hayes, S. T., & Adams, J. A. (2011). A comparison of communicative modes for map-based tasking. In IEEE internation conference on systems, man, and cybernetics.Google Scholar
  25. Johnson, M., Shrewsbury, B., Bertrand, S., Wu, T., Duran, D., Floyd, M., et al. (2015). Team ihmc’s lessons learned from the darpa robotics challenge trials. Journal of Field Robotics, 32(2), 192–208.CrossRefGoogle Scholar
  26. Jones, H. L., Rock, S. M., Burns, D., & Morris, S. (2002). Autonomous robots in SWAT applications: Research, design, and operations challenges. AUVSI.Google Scholar
  27. Kahneman, D., & Tversky, A. (1977). Intuitive prediction: Biases and corrective procedures. Technical Report.Google Scholar
  28. Kidd, C. D., & Breazeal, C. (2004). Effect of a robot on user perceptions. In IEEE/RSJ international conference on intelligent robots and systems IROS 2004, Vol 4, (pp. 3559–3564).Google Scholar
  29. Lee, K. M., Jung, Y., Kim, J., & Kim, S. R. (2006). Are physically embodied social agents better than disembodied social agents?: The effects of physical embodiment, tactile interaction, and people’s loneliness in humanrobot interaction. International Journal of Human-Computer Studies, 64(10), 962–973.CrossRefGoogle Scholar
  30. Macho, S., Torrens, M., & Faltings, B. (2000). A multi-agent recommender system for planning meetings. In Proceedings of workshop on agent-based recommender systems, autonomous agents 2000, ACM.Google Scholar
  31. Mackenzie, C. F., Xiao, Y., & Horst, R. (2004). Video task analysis in high performance teams. Cognition, Technology, and Work, 6, 139–147.CrossRefGoogle Scholar
  32. Murphy, R. R. (2015). Meta-analysis of autonomy at the darpa robotics challenge trials. Journal of Field Robotics, 32(2), 189–191.CrossRefGoogle Scholar
  33. Muscettola, N., Morris, P., & Tsamardinos, I. (1998). Reformulating temporal plans for efficient execution. In Proceedings of the 6th international conference on principles of knowledge representation and reasoning (KR&R), Trento.Google Scholar
  34. Nikolaidis, S., & Shah, J. (2013). Human-robot cross-training: Computational formulation, modeling and evaluation of a human team training strategy. In Proceedings of the international conference on human-robot interaction (HRI) (pp. 33–40).Google Scholar
  35. Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors: The Journal of the Human Factors and Ergonomics Society, 52, 381–410.CrossRefGoogle Scholar
  36. Parasuraman, R., Mustapha, M., & Hilburn, B. (1999). Adaptive aiding and adpative task allocation enchance human-machine systems. Automation Technology and Human Performance: Current Research and Trends, 1999, 119–123.Google Scholar
  37. Powers, A., Kiesler, S., Fussell, S., & Torrey, C. (2007). Comparing a computer agent with a humanoid robot. In 2nd ACM/IEEE international conference on human-robot interaction (HRI) (pp. 145–152).Google Scholar
  38. Ryan, J. C., Banerjee, A. G., Cummings, M. L., & Roy, N. (2013). Comparing the performance of expert user heuristics and an integer linear program in aircraft carrier deck operations. IEEE Transaction on Cybernetics, 9(4), 669–678.Google Scholar
  39. Salas, E., Fowlkes, J. E., Stout, R. J., Milanovich, D. M., & Prince, C. (1999). Does CRM training improve teamwork skills in the cockpit? Two evaluation studies. Human Factors, 41, 326–343.CrossRefGoogle Scholar
  40. Shah, J., Wiken, J., Williams, B., & Breazeal, C. (2011). Improved human-robot team performance using chaski, a human-inspired plan execution system. In Proceedings of the international conference on human-robot interaction (HRI) (pp. 29–36).Google Scholar
  41. Stentz, A., Herman, H., Kelly, A., Meyhofer, E., Clark Haynes, G., Stager, D., et al. (2015). Chimp, the cmu highly intelligent mobile platform. Journal of Field Robotics, 32(2), 209–228.CrossRefGoogle Scholar
  42. Volpe, C., Cannon-Bowers, J., Salas, E., & Spector, P. (1996). The impact of cross training on team functioning. Human Factors, 38, 87–100.CrossRefGoogle Scholar
  43. Wainer, J., Feil-Seifer, D. J., Shell, D. A., Matarić, M. J. (2007). Embodiment and human-robot interaction: A task-based perspective. In The 16th IEEE international symposium on robot and human interactive communication (pp. 872–877).Google Scholar
  44. Wilcox, R. J., Nikolaidis, S., & Shah, J. A. (2012). Optimization of temporal dynamics for adaptive human-robot interaction in assembly manufacturing. In Proceedings of robotics: science and systems (RSS) (pp. 9–13). Sydney.Google Scholar
  45. Zhang, H., Law, E., Miller, R., Gajos, K., Parkes, D., & Horvitz, E. (2012). Human computation tasks with global constraints. Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’12 (pp. 217–226). New York: ACM.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Matthew C. Gombolay
    • 1
    Email author
  • Reymundo A. Gutierrez
    • 1
  • Shanelle G. Clarke
    • 1
  • Giancarlo F. Sturla
    • 1
  • Julie A. Shah
    • 1
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations