Human Intention Detection as a Multiclass Classification Problem: Application in Physical Human–Robot Interaction While Walking

@article{Lanini2018HumanID,
  title={Human Intention Detection as a Multiclass Classification Problem: Application in Physical Human–Robot Interaction While Walking},
  author={Jessica Lanini and Hamed Razavi and Julen Urain and Auke Jan Ijspeert},
  journal={IEEE Robotics and Automation Letters},
  year={2018},
  volume={3},
  pages={4171-4178}
}
In many physical human–robot interaction scenarios, for successful completion of the tasks, robots should be able to recognize the human partner's intention. One of such scenarios that is studied in this letter is the collaborative task of carrying an object by a human–humanoid pair in which the humanoid should be able to interpret specific human partner's intentions (e.g., start/stop-walking, accelerate, etc.) only through haptic feedback. To address this problem, we first performed human… CONTINUE READING
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Key Quantitative Results

  • With supervised learning on human-human experiments we identified the minimal set of variables that result in an accuracy above 90% in intention detection for a specific set of commands, in contrast to previous works where the choice of a specific variable, as well as its value, is made according to some heuristics or through simple observations.

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