research-article

Human-robot cross-training: computational formulation, modeling and evaluation of a human team training strategy

Authors:

Stefanos Nikolaidis,

Julie ShahAuthors Info & Claims

HRI '13: Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction

Pages 33 - 40

Published: 03 March 2013 Publication History

Abstract

We design and evaluate human-robot cross-training, a strategy widely used and validated for effective human team training. Cross-training is an interactive planning method in which a human and a robot iteratively switch roles to learn a shared plan for a collaborative task.

We first present a computational formulation of the robot's interrole knowledge and show that it is quantitatively comparable to the human mental model. Based on this encoding, we formulate human-robot cross-training and evaluate it in human subject experiments (n = 36). We compare human-robot cross-training to standard reinforcement learning techniques, and show that cross-training provides statistically significant improvements in quantitative team performance measures. Additionally, significant differences emerge in the perceived robot performance and human trust. These results support the hypothesis that effective and fluent human-robot teaming may be best achieved by modeling effective practices for human teamwork.

References

[1]

P. Abbeel and A. Y. Ng, "Apprenticeship learning via inverse reinforcement learning," in Proc. ICML. ACM Press, 2004.

Digital Library

[2]

B. Akgun, M. Cakmak, J. W. Yoo, and A. L. Thomaz, "Trajectories and keyframes for kinesthetic teaching: a human-robot interaction perspective," in HRI, 2012, pp. 391--398.

Digital Library

[3]

T. Arai, R. Kato, and M. Fujita, "Assessment of operator stress induced by robot collaboration in assembly," CIRP Annals - Manufacturing Technology, vol. 59, no. 1, pp. 5--8, 2010.

[4]

B. D. Argall, S. Chernova, M. Veloso, and B. Browning, "A survey of robot learning from demonstration," Robot. Auton. Syst., vol. 57, no. 5, pp. 469--483, May 2009.

Digital Library

[5]

C. G. Atkeson and S. Schaal, "Robot learning from demonstration," in ICML, 1997, pp. 12--20.

Digital Library

[6]

B. Blumberg, M. Downie, Y. Ivanov, M. Berlin, M. P. Johnson, and B. Tomlinson, "Integrated learning for interactive synthetic characters," ACM Trans. Graph., vol. 21, no. 3, pp. 417--426, Jul. 2002.

Digital Library

[7]

B. E. B. C. Cannon-Bowers J.A., Salas E., "The impact of cross-training and workload on team functioning: a replication and extension of initial findings." Human Factors, pp. 92--101, 1998.

[8]

S. Chernova and M. Veloso, "Multi-thresholded approach to demonstration selection for interactive robot learning," in Proc. HRI. New York, NY, USA: ACM, 2008, pp. 225--232.

Digital Library

[9]

___, "Teaching multi-robot coordination using demonstration of communication and state sharing," in Proc. AAMAS, Richland, SC, 2008.

Digital Library

[10]

F. Doshi and N. Roy, "Efficient model learning for dialog management," in Proc. HRI, Washington, DC, March 2007.

Digital Library

[11]

L. Ekroot and T. Cover, "The entropy of markov trajectories," Information Theory, IEEE Transactions on, vol. 39, no. 4, pp. 1418--1421, jul 1993.

Digital Library

[12]

G. Hoffman and C. Breazeal, "Effects of anticipatory action on humanrobot teamwork efficiency, fluency, and perception of team," in Proc. HRI. New York, NY, USA: ACM, 2007, pp. 1--8.

Digital Library

[13]

F. Kaplan, P.-Y. Oudeyer, E. Kubinyi, and A. Miklósi, "Robotic clicker training," Robotics and Autonomous Systems, pp. 197--206, 2002.

[14]

W. B. Knox and P. Stone, "Interactively shaping agents via human reinforcement: The tamer framework," in Proc. K-CAP, September 2009.

Digital Library

[15]

___, "Combining manual feedback with subsequent mdp reward signals for reinforcement learning," in Proc. AAMAS, May 2010.

Digital Library

[16]

___, "Reinforcement learning from simultaneous human and mdp reward," in Proc. AAMAS, June 2012.

Digital Library

[17]

J. Langan-Fox, S. Code, and K. Langfield-Smith, "Team mental models: Techniques, methods, and analytic approaches," Human Factors, 2000.

[18]

M. Marks, M. Sabella, C. Burke, and S. Zaccaro, "The impact of crosstraining on team effectiveness," J Appl Psychol, pp. 3--13, 2002.

[19]

M. A. Marks, S. J. Zaccaro, and J. E. Mathieu, "Performance implications of leader briefings and team-interaction training for team adaptation to novel environments," J Appl Psychol, vol. 85, pp. 971--986, 2000.

[20]

N. Navarro, C. Weber, and S. Wermter, "Real-world reinforcement learning for autonomous humanoid robot charging in a home environment," in Proc. TAROS. Berlin, Heidelberg: Springer-Verlag, 2011, pp. 231--240.

Digital Library

[21]

M. N. Nicolescu and M. J. Mataric, "Natural methods for robot task learning: Instructive demonstrations, generalization and practice," in Proc. AAMAS, 2003, pp. 241--248.

Digital Library

[22]

S. Nikolaidis and J. Shah, "Human-robot interactive planning using cross-training: A human team training approach," in Proc. Infotech, June 2012.

[23]

(2012) Phasespace motion capture http://www.phasespace.com.

[24]

D. Ramachandran and R. Gupta, "Smoothed sarsa: reinforcement learning for robot delivery tasks," in Proc. ICRA. Piscataway, NJ, USA: IEEE Press, 2009, pp. 3327--3334.

Digital Library

[25]

S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson Education, 2003.

Digital Library

[26]

J. Shah, J. Wiken, B. Williams, and C. Breazeal, "Improved humanrobot team performance using chaski, a human-inspired plan execution system," in Proc. HRI. New York, NY, USA: ACM, 2011, pp. 29--36.

Digital Library

[27]

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press, 1998.

Digital Library

[28]

A. C. Tenorio-Gonzalez, E. F. Morales, and L. Villaseñor Pineda, "Dynamic reward shaping: training a robot by voice," in Proc. IBERAMIA. Berlin, Heidelberg: Springer-Verlag, 2010, pp. 483--492.

Digital Library

[29]

A. L. Thomaz and C. Breazeal, "Reinforcement learning with human teachers: evidence of feedback and guidance with implications for learning performance," in Proc. AAAI, 2006, pp. 1000--1005.

Digital Library

[30]

A. L. Thomaz, G. Hoffman, and C. Breazeal, "C.: Real-time interactive reinforcement learning for robots," in Proc. of AAAI Workshop on Human Comprehensible Machine Learning, 2005.

[31]

K. Waugh, B. D. Ziebart, and J. A. D. Bagnell, "Computational rationalization: The inverse equilibrium problem," in Proc. ICML, June 2011.

Cited By

Ma LIjtsma MFeigh KPritchett A(2022)Metrics for Human-Robot Team Design: A Teamwork Perspective on Evaluation of Human-Robot TeamsACM Transactions on Human-Robot Interaction10.1145/352258111:3(1-36)Online publication date: 2-Sep-2022
https://dl.acm.org/doi/10.1145/3522581
Liu RNatarajan MGombolay M(2021)Coordinating Human-Robot Teams with Dynamic and Stochastic Task ProficienciesACM Transactions on Human-Robot Interaction10.1145/347739111:1(1-42)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3477391
Carroll MShah RHo MGriffiths TSeshia SAbbeel PDragan AWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)On the utility of learning about humans for human-AI coordinationProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454752(5174-5185)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3454752
Show More Cited By

Index Terms

Human-robot cross-training: computational formulation, modeling and evaluation of a human team training strategy
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
  2. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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Information

Published In

cover image ACM Conferences

HRI '13: Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction

March 2013

452 pages

ISBN:9781467330558

General Chairs:
Hideaki Kuzuoka
University of Tsukuba, Japan
,
Vanessa Evers
University of Twente, The Netherlands
,
Program Chairs:
Michita Imai
Keio University, Japan
,
Jodi Forlizzi
Carnegie Mellon University, USA

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence
RA: IEEE Robotics and Automation Society
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

In-Cooperation

AAAI: American Association for Artificial Intelligence
Human Factors & Ergonomics Soc: Human Factors & Ergonomics Soc

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IEEE Press

Publication History

Published: 03 March 2013

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HRI'13

Sponsor:

HRI'13: ACM/IEEE International Conference on Human-Robot Interaction

March 3 - 6, 2013

Tokyo, Japan

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Overall Acceptance Rate 268 of 1,124 submissions, 24%

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Cited By

Ma LIjtsma MFeigh KPritchett A(2022)Metrics for Human-Robot Team Design: A Teamwork Perspective on Evaluation of Human-Robot TeamsACM Transactions on Human-Robot Interaction10.1145/352258111:3(1-36)Online publication date: 2-Sep-2022
https://dl.acm.org/doi/10.1145/3522581
Liu RNatarajan MGombolay M(2021)Coordinating Human-Robot Teams with Dynamic and Stochastic Task ProficienciesACM Transactions on Human-Robot Interaction10.1145/347739111:1(1-42)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3477391
Carroll MShah RHo MGriffiths TSeshia SAbbeel PDragan AWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)On the utility of learning about humans for human-AI coordinationProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454752(5174-5185)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3454752
Tabrez AAgrawal SHayes BKim JTapus ASirkin DJung MKwak S(2019)Explanation-based reward coaching to improve human performance via reinforcement learningProceedings of the 14th ACM/IEEE International Conference on Human-Robot Interaction10.5555/3378680.3378717(249-257)Online publication date: 11-Mar-2019
https://dl.acm.org/doi/10.5555/3378680.3378717
Ravula MAlkoby SStone P(2019)Ad hoc teamwork with behavior switching agentsProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367032.3367111(550-556)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367032.3367111
Pandya RHuang SHadfield-Menell DDragan AConitzer VHadfield GVallor S(2019)Human-AI Learning Performance in Multi-Armed BanditsProceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3306618.3314245(369-375)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1145/3306618.3314245
Kyrarini MHaseeb MRistić-Durrant DGräser A(2019)Robot learning of industrial assembly task via human demonstrationsAutonomous Robots10.1007/s10514-018-9725-643:1(239-257)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s10514-018-9725-6
Amir ODoshi-Velez FSarne DAndre EKoenig SDastani MSukthankar G(2018)Agent Strategy SummarizationProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237877(1203-1207)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.5555/3237383.3237877
Amir DAmir OAndre EKoenig SDastani MSukthankar G(2018)HIGHLIGHTSProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237869(1168-1176)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.5555/3237383.3237869
Levine SWilliams B(2018)Watching and acting togetherJournal of Artificial Intelligence Research10.1613/jair.1.1124363:1(281-359)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1613/jair.1.11243
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