Generative Attention Learning: a "GenerAL" framework for high-performance multi-fingered grasping in clutter

@article{Wu2020GenerativeAL,
  title={Generative Attention Learning: a "GenerAL" framework for high-performance multi-fingered grasping in clutter},
  author={Bo-Han Wu and Iretiayo Akinola and Abhi Gupta and F. Xu and J. Varley and David Watkins-Valls and P. Allen},
  journal={Auton. Robots},
  year={2020},
  volume={44},
  pages={971-990}
}
  • Bo-Han Wu, Iretiayo Akinola, +4 authors P. Allen
  • Published 2020
  • Computer Science
  • Auton. Robots
    • Generative Attention Learning (GenerAL) is a framework for high-DOF multi-fingered grasping that is not only robust to dense clutter and novel objects but also effective with a variety of different parallel-jaw and multi-fingered robot hands. This framework introduces a novel attention mechanism that substantially improves the grasp success rate in clutter. Its generative nature allows the learning of full-DOF grasps with flexible end-effector positions and orientations, as well as all finger… CONTINUE READING
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