Abstract
The usefulness of shared-control assistive robots frequently relies on the underlying autonomous agent’s ability to infer human intentions unambiguously, often from low-dimensional and noisy signals generated by the human through a control interface. In this paper, we propose a strategy in which the autonomous agent nudges the context in which the human generates their control actions. In doing so, the autonomous agent attempts to improve its own ability to infer intent accurately, which in turn allows it to provide more accurate assistance. The contributions of this paper are three-fold. First, we introduce an interface-aware information-theoretic metric for active disambiguation that aims to characterize world states according to their potential to extract maximally intent-expressive control actions from the user. Second, we propose a turn-taking based human-autonomy interaction protocol in which the autonomous agent utilizes the disambiguation metric to help itself reduce the uncertainty of its prediction of human intent. Third, we evaluate our metric and interaction protocol both in simulation and with a 9-person human subject study. Our results suggest that disambiguation (a) helps to significantly reduce task effort, as measured by number of mode switches, task completion times, and number of turns executed by the human, and (b) enables the autonomous agent to provide accurate assistance with greater contribution to the overall control signal.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Atanasov, N., Le Ny, J., Daniilidis, K., Pappas, G.J.: Information acquisition with sensing robots: algorithms and error bounds. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2014)
Barfoot, T.D.: State Estimation for Robotics. Cambridge University Press, Cambridge (2017)
Brooks, C., Szafir, D.: Balanced information gathering and goal-oriented actions in shared autonomy. In: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 85–94. IEEE (2019)
Callaway, F., Hardy, M., Griffiths, T.: Optimal nudging. In: CogSci (2020)
Ewert, B., Loer, K., Thomann, E.: Beyond nudge: advancing the state-of-the-art of behavioural public policy and administration. Policy & Polit. 49(1), 3–23 (2021)
Gopinath, D., Javaremi, M.N., Argall, B.: Customized handling of unintended interface operation in assistive robots. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 10406–10412 (2021)
Gopinath, D.E., Argall, B.D.: Active intent disambiguation for shared control robots. IEEE Trans. Neural Syst. Rehabil. Eng. 28(6), 1497–1506 (2020)
Gopinath, D.E., Argall, B.D.: Mode switch assistance to maximize human intent disambiguation. In: Robotics: Science and Systems (2017)
Hart, S.G.: NASA-task load index (NASA-TLX); 20 years later. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting (2006)
Herlant, L.V., Holladay, R.M., Srinivasa, S.S.: Assistive teleoperation of robot arms via automatic time-optimal mode switching. In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI) (2016)
Huber, L., Billard, A., Slotine, J.J.: Avoidance of convex and concave obstacles with convergence ensured through contraction. IEEE Robot. Autom. Lett. 4(2), 1462–1469 (2019)
Javdani, S., Admoni, H., Pellegrinelli, S., Srinivasa, S.S., Bagnell, J.A.: Shared autonomy via hindsight optimization for teleoperation and teaming (2017). arXiv:1706.00155
Johnson, E.J., Shu, S.B., Dellaert, B.G., Fox, C., Goldstein, D.G., Häubl, G., Larrick, R.P., Payne, J.W., Peters, E., Schkade, D., et al.: Beyond nudges: tools of a choice architecture. Mark. Lett. 23(2), 487–504 (2012)
Lala, D., Inoue, K., Kawahara, T.: Smooth turn-taking by a robot using an online continuous model to generate turn-taking cues. In: 2019 International Conference on Multimodal Interaction, pp. 226–234 (2019)
LaPlante, M.P., et al.: Assistive technology devices and home accessibility features: prevalence, payment, need, and trends. Advance Data from Vital and Health Statistics (1992)
Miller, L.M., Silverman, Y., MacIver, M.A., Murphey, T.D.: Ergodic exploration of distributed information. IEEE Trans. Robot. 32(1), 36–52 (2016)
Robotics, C.S.S., Nørskov, M., et al.: Nudging by social robots. In: Culturally Sustainable Social Robotics: Proceedings of Robophilosophy 2020, vol. 335, p. 337 (2021)
Sadigh, D., Sastry, S.S., Seshia, S.A., Dragan, A.: Information gathering actions over human internal state. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 66–73. IEEE (2016)
Skantze, G.: Turn-taking in conversational systems and human-robot interaction: a review. Comput. Speech & Lang. 67, 101178 (2021)
Soleiman, P., Moradi, H., Mahmoudi, M., Teymouri, M., Pouretemad, H.R.: Teaching turn-taking skills to children with autism using a parrot-like robot (2021). arXiv:2101.12273
Thaler, R.H.: From cashews to nudges: the evolution of behavioral economics. Amer. Econ. Rev. 108(6), 1265–87 (2018)
Thaler, R.H., Sunstein, C.R.: Nudge: improving decisions about health, wealth, and happiness (2008)
Thomaz, A.L., Chao, C.: Turn-taking based on information flow for fluent human-robot interaction. AI Mag. 32(4), 53–63 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gopinath, D.E., Thompson, A., Argall, B.D. (2023). Information Theoretic Intent Disambiguation via Contextual Nudges for Assistive Shared Control. In: LaValle, S.M., O’Kane, J.M., Otte, M., Sadigh, D., Tokekar, P. (eds) Algorithmic Foundations of Robotics XV. WAFR 2022. Springer Proceedings in Advanced Robotics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-031-21090-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-21090-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21089-1
Online ISBN: 978-3-031-21090-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)