Autonomous human–robot proxemics: socially aware navigation based on interaction potential

Abstract

To enable situated human–robot interaction (HRI), an autonomous robot must both understand and control proxemics—the social use of space—to employ natural communication mechanisms analogous to those used by humans. This work presents a computational framework of proxemics based on data-driven probabilistic models of how social signals (speech and gesture) are produced (by a human) and perceived (by a robot). The framework and models were implemented as autonomous proxemic behavior systems for sociable robots, including: (1) a sampling-based method for robot proxemic goal state estimation with respect to human–robot distance and orientation parameters, (2) a reactive proxemic controller for goal state realization, and (3) a cost-based trajectory planner for maximizing automated robot speech and gesture recognition rates along a path to the goal state. Evaluation results indicate that the goal state estimation and realization significantly improve upon past work in human–robot proxemics with respect to “interaction potential”—predicted automated speech and gesture recognition rates as the robot enters into and engages in face-to-face social encounters with a human user—illustrating their efficacy to support richer robot perception and autonomy in HRI.

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Notes

  1. 1.

    https://dev.windows.com/kinect.

  2. 2.

    https://software.intel.com/realsense.

  3. 3.

    In practice, we often extend the model as a dynamic Bayesian network (Rabiner 1990) by conditioning the pose on the previous state during resampling to ensure that the pose does not change drastically between time intervals (Mead and Matarić 2016). For interactions between two agents, this level of inference might be excessive; however, for interactions between three or more agents, such inference is effective in determining a stable set of proxemic parameters.

  4. 4.

    In practice, a small number (in this work, \(10^{-6}\)) is added to IP to prevent division-by-zero errors when calculating the weight \(w_{IP}^{t}\) (Eq. 7).

  5. 5.

    http://wiki.ros.org/navigation.

  6. 6.

    http://www.ros.org.

  7. 7.

    The objective metrics employed did not necessitate a diverse set of human participants.

  8. 8.

    http://gazebosim.org.

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Acknowledgments

This work was supported in part by an NSF Graduate Research Fellowship, the NSF National Robotics Initiative Grant IIS-1208500, and NSF Grants CNS-0709296 and CNS-1513275.

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Correspondence to Ross Mead.

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Mead, R., Matarić, M.J. Autonomous human–robot proxemics: socially aware navigation based on interaction potential. Auton Robot 41, 1189–1201 (2017). https://doi.org/10.1007/s10514-016-9572-2

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Keywords

  • Human–robot interaction
  • Proxemics
  • Social signals
  • Interaction potential
  • Goal state estimation
  • Path-planning