Reconfigurable Design for Omni-adaptive Grasp Learning

• URL: http://arxiv.org/abs/2003.01582v1
• Date: Sat, 29 Feb 2020 20:48:58 GMT
• Title: Reconfigurable Design for Omni-adaptive Grasp Learning
• Abstract summary: We adopt the reconfigurable design of the robotic gripper using a novel soft finger structure with omni-directional adaptation. A 3-finger and 4-finger radial configuration is the most effective one achieving an average 96% grasp success rate.
• Score: 14.25785215435502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The engineering design of robotic grippers presents an ample design space for optimization towards robust grasping. In this paper, we adopt the reconfigurable design of the robotic gripper using a novel soft finger structure with omni-directional adaptation, which generates a large number of possible gripper configurations by rearranging these fingers. Such reconfigurable design with these 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 a 3-finger and 4-finger radial configuration is the most effective one achieving an average 96\% grasp success rate on seen and novel objects selected from the YCB dataset. We also discussed the influence of the frictional surface on the finger to improve the grasp robustness.

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