A Reconfigurable Design for Omni-Adaptive Grasp Learning

@article{Wan2020ARD,
  title={A Reconfigurable Design for Omni-Adaptive Grasp Learning},
  author={Fang Wan and Haokun Wang and Jiyuan Wu and Yujia Liu and Sheng Ge and Chaoyang Song},
  journal={IEEE Robotics and Automation Letters},
  year={2020},
  volume={5},
  pages={4210-4217}
}
The engineering design of robotic grippers presents an ample design space for optimization towards robust grasping. In this letter, we investigate how learning method can be used to support the design reconfiguration of robotic grippers for grasping using a novel soft structure with omni-directional adaptation. We propose a gripper system that is reconfigurable in terms of the number and arrangement of the proposed finger, which generates a large number of possible design configurations. Such… 

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