Transferring human navigation behaviors into a robot local planner

Rafael Ramón-Vigo, Noé Pérez-Higueras, Fernando Caballero, and Luis Merino. Transferring human navigation behaviors into a robot local planner. In Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN, 2014.

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Abstract

Robot navigation in human environments is an active research area that poses serious challenges. Among them, social navigation and human-awareness has gain lot of attention in the last years due to its important role in human safety and robot acceptance. Learning has been proposed as a more principled way of estimating the insights of human social interactions. In this paper, inverse reinforcement learning is analyzed as a tool to transfer the typical human navigation behavior to the robot local navigation planner. Observations of real human motion interactions found in one publicly available datasets are employed to learn a cost function, which is then used to determine a navigation controller. The paper presents an analysis of the performance of the controller behavior in two different scenarios interacting with persons, and a comparison of this approach with a Proxemics-based method.

BibTeX Entry

@INPROCEEDINGS{roman14,
  author = {Rafael Ram\'{o}n-Vigo and No\'{e} P\'{e}rez-Higueras and Fernando Caballero and Luis Merino},
  title = {Transferring human navigation behaviors into a robot local planner},
  booktitle = {Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN},
  year = {2014},
  doi = {10.1109/ROMAN.2014.6926347},
   abstract={Robot navigation in human environments is an active research area that poses serious challenges. Among them, social navigation and human-awareness has gain lot of attention in the last years due to its important role in human safety and robot acceptance. Learning has been proposed as a more principled way of estimating the insights of human social interactions. In this paper, inverse reinforcement learning is analyzed as a tool to transfer the typical human navigation behavior to the robot local navigation planner. Observations of real human motion interactions found in one publicly available datasets  are employed to learn a cost function, which is then used to determine a navigation controller. The paper presents an analysis of the performance of the controller behavior in two different scenarios interacting with persons, and a comparison of this approach with a Proxemics-based method.},
}

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