Recursive Bayesian Human Intent Recognition in Shared-Control Robotics

Siddarth Jain, Brenna Dee Argall

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Effective human-robot collaboration in shared control requires reasoning about the intentions of the human user. In this work, we present a mathematical formulation for human intent recognition during assistive teleoperation under shared autonomy. Our recursive Bayesian filtering approach models and fuses multiple non-verbal observations to probabilistically reason about the intended goal of the user. In addition to contextual observations, we model and incorporate the human agent's behavior as goal-directed actions with adjustable rationality to inform the underlying intent. We examine human inference on robot motion and furthermore validate our approach with a human subjects study that evaluates autonomy intent inference performance under a variety of goal scenarios and tasks, by novice subjects. Results show that our approach outperforms existing solutions and demonstrates that the probabilistic fusion of multiple observations improves intent inference and performance for shared-control operation.

Original languageEnglish (US)
Title of host publication2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3905-3912
Number of pages8
ISBN (Electronic)9781538680940
DOIs
StatePublished - Dec 27 2018
Event2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 - Madrid, Spain
Duration: Oct 1 2018Oct 5 2018

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
CountrySpain
CityMadrid
Period10/1/1810/5/18

Fingerprint

Robotics
Robots
Electric fuses
Remote control
Fusion reactions

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Jain, S., & Argall, B. D. (2018). Recursive Bayesian Human Intent Recognition in Shared-Control Robotics. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 (pp. 3905-3912). [8593766] (IEEE International Conference on Intelligent Robots and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2018.8593766
Jain, Siddarth ; Argall, Brenna Dee. / Recursive Bayesian Human Intent Recognition in Shared-Control Robotics. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 3905-3912 (IEEE International Conference on Intelligent Robots and Systems).
@inproceedings{10db49729a4d412c90713b5e9c1acbec,
title = "Recursive Bayesian Human Intent Recognition in Shared-Control Robotics",
abstract = "Effective human-robot collaboration in shared control requires reasoning about the intentions of the human user. In this work, we present a mathematical formulation for human intent recognition during assistive teleoperation under shared autonomy. Our recursive Bayesian filtering approach models and fuses multiple non-verbal observations to probabilistically reason about the intended goal of the user. In addition to contextual observations, we model and incorporate the human agent's behavior as goal-directed actions with adjustable rationality to inform the underlying intent. We examine human inference on robot motion and furthermore validate our approach with a human subjects study that evaluates autonomy intent inference performance under a variety of goal scenarios and tasks, by novice subjects. Results show that our approach outperforms existing solutions and demonstrates that the probabilistic fusion of multiple observations improves intent inference and performance for shared-control operation.",
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Jain, S & Argall, BD 2018, Recursive Bayesian Human Intent Recognition in Shared-Control Robotics. in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018., 8593766, IEEE International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 3905-3912, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018, Madrid, Spain, 10/1/18. https://doi.org/10.1109/IROS.2018.8593766

Recursive Bayesian Human Intent Recognition in Shared-Control Robotics. / Jain, Siddarth; Argall, Brenna Dee.

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3905-3912 8593766 (IEEE International Conference on Intelligent Robots and Systems).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Effective human-robot collaboration in shared control requires reasoning about the intentions of the human user. In this work, we present a mathematical formulation for human intent recognition during assistive teleoperation under shared autonomy. Our recursive Bayesian filtering approach models and fuses multiple non-verbal observations to probabilistically reason about the intended goal of the user. In addition to contextual observations, we model and incorporate the human agent's behavior as goal-directed actions with adjustable rationality to inform the underlying intent. We examine human inference on robot motion and furthermore validate our approach with a human subjects study that evaluates autonomy intent inference performance under a variety of goal scenarios and tasks, by novice subjects. Results show that our approach outperforms existing solutions and demonstrates that the probabilistic fusion of multiple observations improves intent inference and performance for shared-control operation.

AB - Effective human-robot collaboration in shared control requires reasoning about the intentions of the human user. In this work, we present a mathematical formulation for human intent recognition during assistive teleoperation under shared autonomy. Our recursive Bayesian filtering approach models and fuses multiple non-verbal observations to probabilistically reason about the intended goal of the user. In addition to contextual observations, we model and incorporate the human agent's behavior as goal-directed actions with adjustable rationality to inform the underlying intent. We examine human inference on robot motion and furthermore validate our approach with a human subjects study that evaluates autonomy intent inference performance under a variety of goal scenarios and tasks, by novice subjects. Results show that our approach outperforms existing solutions and demonstrates that the probabilistic fusion of multiple observations improves intent inference and performance for shared-control operation.

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Jain S, Argall BD. Recursive Bayesian Human Intent Recognition in Shared-Control Robotics. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 3905-3912. 8593766. (IEEE International Conference on Intelligent Robots and Systems). https://doi.org/10.1109/IROS.2018.8593766