Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning

@article{Yuan2018RearrangementWN,
  title={Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning},
  author={Weihao Yuan and J. Stork and D. Kragic and M. Wang and Kaiyu Hang},
  journal={2018 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2018},
  pages={270-277}
}
  • Weihao Yuan, J. Stork, +2 authors Kaiyu Hang
  • Published 2018
  • Computer Science, Engineering
  • 2018 IEEE International Conference on Robotics and Automation (ICRA)
Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on… Expand
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