A Reconfigurable Design for Omni-adaptive Grasp Learning

Fang Wan, Haokun Wang, Jiyuan Wu, Yujia Liu, Sheng Ge, Chaoyang Song*

The engineering design of robotic grippers presents an ample design space for optimization towards robust grasping. In this paper, we investigate how can learning method 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 design reconfigurations with omni-adaptive fingers enables us to systematically investigate the optimal arrangement of the fingers towards robust grasping. Furthermore, we adopt a learning-based method as the baseline to benchmark the effectiveness of each design configuration. As a result, we found that the 3-finger radial configuration is suitable for space-saving and cost-effectiveness, achieving an average 96% grasp success rate on seen and novel objects selected from the YCB dataset. The 4-finger radial arrangement can be applied to cases that require a higher payload with even distribution. We achieved dimension reduction using the radial gripper design with the removal of z-axis rotation during grasping. We also reported the different outcomes with or without friction enhancement of the soft finger network.

Under review for IEEE Robotics and Automation Letters.
To appear at the IEEE International Conference on Soft Robotics (RoboSoft) 2020.
Latest preprint version (29 Feb 2020): arXiv:2003.01582 [cs.RO].

@misc{Wan2020OmniLearning,
    title={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},
    year={2020},
    eprint={2003.01582},
    archivePrefix={arXiv},
    primaryClass={cs.RO}
}