End-to-End Training of Deep Visuomotor Policies

@article{Levine2016EndtoEndTO,
  title={End-to-End Training of Deep Visuomotor Policies},
  author={Sergey Levine and Chelsea Finn and Trevor Darrell and Pieter Abbeel},
  journal={Journal of Machine Learning Research},
  year={2016},
  volume={17},
  pages={39:1-39:40}
}
Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-toend provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that… CONTINUE READING
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