Maximizing BCI Human Feedback using Active Learning

@article{Wang2020MaximizingBH,
  title={Maximizing BCI Human Feedback using Active Learning},
  author={Zi-zhao Wang and Junyao Shi and Iretiayo Akinola and P. Allen},
  journal={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020},
  pages={10945-10951}
}
  • Zi-zhao Wang, Junyao Shi, +1 author P. Allen
  • Published 2020
  • Computer Science
  • 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • Recent advancements in Learning from Human Feedback present an effective way to train robot agents via inputs from non-expert humans, without a need for a specially designed reward function. However, this approach needs a human to be present and attentive during robot learning to provide evaluative feedback. In addition, the amount of feedback needed grows with the level of task difficulty and the quality of human feedback might decrease over time because of fatigue. To overcome these… CONTINUE READING

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