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REX: Designing User-centered Repair and Explanations to Address Robot Failures

Published: 01 July 2024 Publication History

Abstract

Robots in real-world environments continuously engage with multiple users and encounter changes that lead to unexpected conflicts in fulfilling user requests. Recent technical advancements (e.g., large-language models (LLMs), program synthesis) offer various methods for automatically generating repair plans that address such conflicts. In this work, we understand how automated repair and explanations can be designed to improve user experience with robot failures through two user studies. In our first, online study (n = 162), users expressed increased trust, satisfaction, and utility with the robot performing automated repair and explanations. However, we also identified risk factors—safety, privacy, and complexity—that require adaptive repair strategies. The second, in-person study (n = 24) elucidated distinct repair and explanation strategies depending on the level of risk severity and type. Using a design-based approach, we explore automated repair with explanations as a solution for robots to handle conflicts and failures, complemented by adaptive strategies for risk factors. Finally, we discuss the implications of incorporating such strategies into robot designs to achieve seamless operation among changing user needs and environments.

References

[1]
2015. Temi - The Personal Robot. https://www.robotemi.com/. Accessed: 2024-04-21.
[2]
2022. Prolific: Quickly Find Research Participants You Can Trust. https://www.prolific.co/
[3]
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, 2022. Do as i can, not as i say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691 (2022).
[4]
Rachid Alami, Alin Albu-Schäffer, Antonio Bicchi, Rainer Bischoff, Raja Chatila, Alessandro De Luca, Agostino De Santis, Georges Giralt, Jérémie Guiochet, Gerd Hirzinger, 2006. Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 1–16.
[5]
Zahra Ashktorab, Mohit Jain, Q Vera Liao, and Justin D Weisz. 2019. Resilient chatbots: Repair strategy preferences for conversational breakdowns. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1–12.
[6]
David Bell, Theodora Koulouri, Stanislao Lauria, Robert D Macredie, and James Sutton. 2014. Microblogging as a mechanism for human–robot interaction. Knowledge-Based Systems 69 (2014), 64–77.
[7]
Curt Bererton and Pradeep Khosla. 2002. An analysis of cooperative repair capabilities in a team of robots. In Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), Vol. 1. IEEE, 476–482.
[8]
Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, 2023. Do as i can, not as i say: Grounding language in robotic affordances. In Conference on robot learning. PMLR, 287–318.
[9]
Daniel J Brooks. 2017. A human-centric approach to autonomous robot failures. Ph. D. Dissertation. University of Massachusetts Lowell.
[10]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.
[11]
Bengisu Cagiltay and Bilge Mutlu. 2024. Toward Family-Robot Interactions: A Family-Centered Framework in HRI. (2024).
[12]
Alain Chavaillaz, David Wastell, and Jürgen Sauer. 2016. System reliability, performance and trust in adaptable automation. Applied Ergonomics 52 (2016), 333–342.
[13]
Vijay Chidambaram, Yueh-Hsuan Chiang, and Bilge Mutlu. 2012. Designing persuasive robots: how robots might persuade people using vocal and nonverbal cues. In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction. 293–300.
[14]
Sungwoo Choi, Anna S Mattila, and Lisa E Bolton. 2021. To err is human (-oid): how do consumers react to robot service failure and recovery?Journal of Service Research 24, 3 (2021), 354–371.
[15]
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017. Deep reinforcement learning from human preferences. Advances in neural information processing systems 30 (2017).
[16]
Michael Jae-Yoon Chung and Maya Cakmak. 2020. Iterative repair of social robot programs from implicit user feedback via bayesian inference. interaction 1 (2020), 2.
[17]
Victoria Clarke and Virginia Braun. 2014. Thematic analysis. In Encyclopedia of critical psychology. Springer, 1947–1952.
[18]
Devleena Das, Siddhartha Banerjee, and Sonia Chernova. 2021. Explainable AI for robot failures: Generating explanations that improve user assistance in fault recovery. In Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction. 351–360.
[19]
Agostino De Santis, Bruno Siciliano, Alessandro De Luca, and Antonio Bicchi. 2008. An atlas of physical human–robot interaction. Mechanism and Machine Theory 43, 3 (2008), 253–270.
[20]
Mary Ellen Foster, Andre Gaschler, Manuel Giuliani, Amy Isard, Maria Pateraki, and Ronald PA Petrick. 2012. Two people walk into a bar: Dynamic multi-party social interaction with a robot agent. In Proceedings of the 14th ACM international conference on Multimodal interaction. 3–10.
[21]
Meiyuzi Gao, Philip Kortum, and Frederick Oswald. 2018. Psychometric evaluation of the use (usefulness, satisfaction, and ease of use) questionnaire for reliability and validity. In Proceedings of the human factors and ergonomics society annual meeting, Vol. 62. SAGE Publications Sage CA: Los Angeles, CA, 1414–1418.
[22]
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997 (2023).
[23]
Marc Hanheide, Moritz Göbelbecker, Graham S Horn, Andrzej Pronobis, Kristoffer Sjöö, Alper Aydemir, Patric Jensfelt, Charles Gretton, Richard Dearden, Miroslav Janicek, 2017. Robot task planning and explanation in open and uncertain worlds. Artificial Intelligence 247 (2017), 119–150.
[24]
Hui-Ru Ho, Edward M Hubbard, and Bilge Mutlu. 2024. " It’s Not a Replacement:" Enabling Parent-Robot Collaboration to Support In-Home Learning Experiences of Young Children. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1–18.
[25]
Hui-Ru Ho, Nathan Thomas White, Edward M Hubbard, and Bilge Mutlu. 2023. Designing Parent-child-robot Interactions to Facilitate In-Home Parental Math Talk with Young Children. In Proceedings of the 22nd Annual ACM Interaction Design and Children Conference. 355–366.
[26]
Shanee Honig and Tal Oron-Gilad. 2018. Understanding and resolving failures in human-robot interaction: Literature review and model development. Frontiers in psychology 9 (2018), 861.
[27]
Yaxin Hu, Lingjie Feng, Bilge Mutlu, and Henny Admoni. 2021. Exploring the Role of Social Robot Behaviors in a Creative Activity. In Proceedings of the 2021 ACM Designing Interactive Systems Conference. 1380–1389.
[28]
Jiun-Yin Jian, Ann Bisantz, and Colin Drury. 2000. Foundations for an Empirically Determined Scale of Trust in Automated Systems. International Journal of Cognitive Ergonomics 4 (03 2000), 53–71. https://doi.org/10.1207/S15327566IJCE0401_04
[29]
Barbara Jobstmann, Andreas Griesmayer, and Roderick Bloem. 2005. Program repair as a game. In Computer Aided Verification: 17th International Conference, CAV 2005, Edinburgh, Scotland, UK, July 6-10, 2005. Proceedings 17. Springer, 226–238.
[30]
Lars Johannsmeier and Sami Haddadin. 2016. A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes. IEEE Robotics and Automation Letters 2, 1 (2016), 41–48.
[31]
Malte Jung and Pamela Hinds. 2018. Robots in the Wild: A Time for More Robust Theories of Human-Robot Interaction. J. Hum.-Robot Interact. 7, 1, Article 2 (may 2018), 5 pages. https://doi.org/10.1145/3208975
[32]
Erez Karpas and Daniele Magazzeni. 2020. Automated planning for robotics. Annual Review of Control, Robotics, and Autonomous Systems 3 (2020), 417–439.
[33]
Rituraj Kaushik, Pierre Desreumaux, and Jean-Baptiste Mouret. 2020. Adaptive prior selection for repertoire-based online adaptation in robotics. Frontiers in Robotics and AI 6 (2020), 151.
[34]
Simon Keizer, Mary Ellen Foster, Oliver Lemon, Andre Gaschler, and Manuel Giuliani. 2013. Training and evaluation of an MDP model for social multi-user human-robot interaction. In Proceedings of the SIGDIAL 2013 Conference. 223–232.
[35]
Piyush Khandelwal, Shiqi Zhang, Jivko Sinapov, Matteo Leonetti, Jesse Thomason, Fangkai Yang, Ilaria Gori, Maxwell Svetlik, Priyanka Khante, Vladimir Lifschitz, 2017. Bwibots: A platform for bridging the gap between ai and human–robot interaction research. The International Journal of Robotics Research 36, 5-7 (2017), 635–659.
[36]
Callie Y Kim, Christine P Lee, and Bilge Mutlu. 2024. Understanding Large-Language Model (LLM)-powered Human-Robot Interaction. arXiv preprint arXiv:2401.03217 (2024).
[37]
Amy Koike, Michael Wehner, and Bilge Mutlu. 2024. Sprout: Designing Expressivity for Robots Using Fiber-Embedded Actuator. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. 403–412.
[38]
Li Yang Ku, Dirk Ruiken, Erik Learned-Miller, and Roderic Grupen. 2015. Error detection and surprise in stochastic robot actions. In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids). IEEE, 1096–1101.
[39]
Christine P Lee, Bengisu Cagiltay, and Bilge Mutlu. 2022. The unboxing experience: Exploration and design of initial interactions between children and social robots. In Proceedings of the 2022 CHI conference on human factors in computing systems. 1–14.
[40]
Christine P Lee, Bengisu Cagiltay, Dakota Sullivan, and Bilge Mutlu. 2023. Demonstrating the Potential of Interactive Product Packaging for Enriching Human-Robot Interaction. In Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction. 899–901.
[41]
Min Kyung Lee, Sara Kiesler, Jodi Forlizzi, Siddhartha Srinivasa, and Paul Rybski. 2010. Gracefully mitigating breakdowns in robotic services. In 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 203–210.
[42]
Séverin Lemaignan, Julia Fink, Francesco Mondada, and Pierre Dillenbourg. 2015. You’re doing it wrong! studying unexpected behaviors in child-robot interaction. In Social Robotics: 7th International Conference, ICSR 2015, Paris, France, October 26-30, 2015, Proceedings 7. Springer, 390–400.
[43]
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33 (2020), 9459–9474.
[44]
Dewen Liu, Changfei Li, Jieqiong Zhang, and Weidong Huang. 2023. Robot service failure and recovery: Literature review and future directions. International Journal of Advanced Robotic Systems 20, 4 (2023), 17298806231191606.
[45]
Zeyi Liu, Arpit Bahety, and Shuran Song. 2023. Reflect: Summarizing robot experiences for failure explanation and correction. arXiv preprint arXiv:2306.15724 (2023).
[46]
Arnold Lund. 2001. Measuring Usability with the USE Questionnaire. Usability and User Experience Newsletter of the STC Usability SIG 8 (01 2001).
[47]
Robyn R Lutz and R Woodhouse. 1999. Bi-directional analysis for certification of safety-critical software. In 1st International Software Assurance Certification Conference (ISACC’99), Vol. 2. 3.
[48]
Linxiang Lv, Minxue Huang, Dawei Guan, and Kairui Yang. 2022. Apology or gratitude? The effect of communication recovery strategies for service failures of AI devices. Journal of Travel & Tourism Marketing 39, 6 (2022), 570–587.
[49]
Amama Mahmood, Jeanie W Fung, Isabel Won, and Chien-Ming Huang. 2022. Owning mistakes sincerely: Strategies for mitigating AI errors. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–11.
[50]
Matthew Marge and Alexander I Rudnicky. 2019. Miscommunication detection and recovery in situated human–robot dialogue. ACM Transactions on Interactive Intelligent Systems (TiiS) 9, 1 (2019), 1–40.
[51]
Nora McDonald, Sarita Schoenebeck, and Andrea Forte. 2019. Reliability and Inter-rater Reliability in Qualitative Research: Norms and Guidelines for CSCW and HCI Practice. Proceedings of the ACM on Human-Computer Interaction 3 (11 2019), 1–23. https://doi.org/10.1145/3359174
[52]
Gaspar Isaac Melsion, Rebecca Stower, Katie Winkle, and Iolanda Leite. 2023. What’s at Stake? Robot explanations matter for high but not low-stake scenarios. In 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, 2421–2426.
[53]
Tobias Mettler, Michaela Sprenger, and Robert Winter. 2017. Service robots in hospitals: new perspectives on niche evolution and technology affordances. European Journal of Information Systems 26, 5 (2017), 451–468.
[54]
Joseph E Michaelis, Bengisu Cagiltay, Rabia Ibtasar, and Bilge Mutlu. 2023. " Off Script:" Design Opportunities Emerging from Long-Term Social Robot Interactions In-the-Wild. In Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction. 378–387.
[55]
Bilge Mutlu and Jodi Forlizzi. 2008. Robots in organizations: the role of workflow, social, and environmental factors in human-robot interaction. In Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction. 287–294.
[56]
James O’Keeffe, Danesh Tarapore, Alan G Millard, and Jon Timmis. 2018. Adaptive online fault diagnosis in autonomous robot swarms. Frontiers in Robotics and AI 5 (2018), 131.
[57]
David Porfirio, Allison Sauppé, Aws Albarghouthi, and Bilge Mutlu. 2018. Authoring and verifying human-robot interactions. In Proceedings of the 31st annual acm symposium on user interface software and technology. 75–86.
[58]
David Porfirio, Allison Sauppé, Aws Albarghouthi, and Bilge Mutlu. 2020. Transforming robot programs based on social context. In Proceedings of the 2020 CHI conference on human factors in computing systems. 1–12.
[59]
David Porfirio, Laura Stegner, Maya Cakmak, Allison Sauppé, Aws Albarghouthi, and Bilge Mutlu. 2023. Sketching Robot Programs On the Fly. In Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction. 584–593.
[60]
Pragathi Praveena, Yeping Wang, Emmanuel Senft, Michael Gleicher, and Bilge Mutlu. 2023. Periscope: A Robotic Camera System to Support Remote Physical Collaboration. Proceedings of the ACM on Human-Computer Interaction 7, CSCW2 (2023), 1–39.
[61]
Shreyas Sundara Raman, Vanya Cohen, David Paulius, Ifrah Idrees, Eric Rosen, Ray Mooney, and Stefanie Tellex. 2023. Cape: Corrective actions from precondition errors using large language models. In 2nd Workshop on Language and Robot Learning: Language as Grounding.
[62]
Shreyas Sundara Raman, Vanya Cohen, Eric Rosen, Ifrah Idrees, David Paulius, and Stefanie Tellex. 2022. Planning with large language models via corrective re-prompting. In NeurIPS 2022 Foundation Models for Decision Making Workshop.
[63]
James Reason. 1990. Human error. Cambridge university press.
[64]
Allison Sauppé and Bilge Mutlu. 2015. The social impact of a robot co-worker in industrial settings. In Proceedings of the 33rd annual ACM conference on human factors in computing systems. 3613–3622.
[65]
Wenguo Shen and Yongwei Wang. 2022. Facilitation of Customer Empathy: The Effect of Robot Apology on Customer Reaction Following a Service Failure.Journal of Marketing Development & Competitiveness 16, 2 (2022).
[66]
Elaine Short, Justin Hart, Michelle Vu, and Brian Scassellati. 2010. No fair!! an interaction with a cheating robot. In 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 219–226.
[67]
Gerald Steinbauer. 2013. A survey about faults of robots used in robocup. In RoboCup 2012: Robot Soccer World Cup XVI 16. Springer, 344–355.
[68]
Dakota Sullivan, Nathan Thomas White, Andrew Schoen, and Bilge Mutlu. 2024. Making Informed Decisions: Supporting Cobot Integration Considering Business and Worker Preferences. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. 706–714.
[69]
Leimin Tian, Pamela Carreno-Medrano, Aimee Allen, Shanti Sumartojo, Michael Mintrom, Enrique Coronado Zuniga, Gentiane Venture, Elizabeth Croft, and Dana Kulic. 2021. Redesigning human-robot interaction in response to robot failures: a participatory design methodology. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. 1–8.
[70]
Leimin Tian and Sharon Oviatt. 2021. A taxonomy of social errors in human-robot interaction. ACM Transactions on Human-Robot Interaction (THRI) 10, 2 (2021), 1–32.
[71]
Siran Wang, Lingli Wang, and Qiang Yan. 2022. Exploring Appropriate Communication Styles for Personalized Chatbots in Service Recovery. In Pacific Asia Conference on Information Systems. 1.
[72]
Xinru Wang and Ming Yin. 2021. Are explanations helpful? a comparative study of the effects of explanations in ai-assisted decision-making. In 26th international conference on intelligent user interfaces. 318–328.
[73]
Jörg Weber and Franz Wotawa. 2012. Diagnosis and repair of dependent failures in the control system of a mobile autonomous robot. Applied intelligence 36, 3 (2012), 511–528.
[74]
David D Woods. 2018. The theory of graceful extensibility: basic rules that govern adaptive systems. Environment Systems and Decisions 38, 4 (2018), 433–457.
[75]
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023. Autogen: Enabling next-gen llm applications via multi-agent conversation framework. arXiv preprint arXiv:2308.08155 (2023).
[76]
Hongyan Yang, Hong Xu, Yan Zhang, Yan Liang, and Ting Lyu. 2022. Exploring the effect of humor in robot failure. Annals of Tourism Research 95 (2022), 103425.
[77]
Qian Yang, Aaron Steinfeld, and John Zimmerman. 2019. Unremarkable AI: Fitting intelligent decision support into critical, clinical decision-making processes. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1–11.
[78]
Angeliki Zacharaki, Ioannis Kostavelis, Antonios Gasteratos, and Ioannis Dokas. 2020. Safety bounds in human robot interaction: A survey. Safety science 127 (2020), 104667.
[79]
Tao Zhang, Chao Feng, Hui Chen, and Junjie Xian. 2022. Calming the customers by AI: Investigating the role of chatbot acting-cute strategies in soothing negative customer emotions. Electronic Markets 32, 4 (2022), 2277–2292.

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cover image ACM Conferences
DIS '24: Proceedings of the 2024 ACM Designing Interactive Systems Conference
July 2024
3616 pages
ISBN:9798400705830
DOI:10.1145/3643834
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 01 July 2024

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Author Tags

  1. failures
  2. human-robot interaction
  3. program repair
  4. robot
  5. user-centered design
  6. vignette study

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DIS '24: Designing Interactive Systems Conference
July 1 - 5, 2024
Copenhagen, Denmark

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