Accelerated Robot Learning via Human Brain Signals

@article{Akinola2020AcceleratedRL,
  title={Accelerated Robot Learning via Human Brain Signals},
  author={Iretiayo Akinola and Zizhao Wang and Junyao Shi and Xiaomin He and Pawan Lapborisuth and Jingxi Xu and David Watkins-Valls and P. Sajda and P. Allen},
  journal={2020 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2020},
  pages={3799-3805}
}
  • Iretiayo Akinola, Zizhao Wang, +6 authors P. Allen
  • Published 2020
  • Computer Science
  • 2020 IEEE International Conference on Robotics and Automation (ICRA)
    • In reinforcement learning (RL), sparse rewards are a natural way to specify the task to be learned. However, most RL algorithms struggle to learn in this setting since the learning signal is mostly zeros. In contrast, humans are good at assessing and predicting the future consequences of actions and can serve as good reward/policy shapers to accelerate the robot learning process. Previous works have shown that the human brain generates an error-related signal, measurable using… CONTINUE READING
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