Reconfigurable Design for Omni-adaptive Grasp Learning
This work addresses the challenge of designing effective robotic grippers for manipulation tasks, presenting an incremental improvement through systematic configuration optimization.
The paper tackled the problem of optimizing robotic gripper design for robust grasping by using a reconfigurable soft finger structure with omni-directional adaptation, resulting in a 3-finger and 4-finger radial configuration achieving a 96% average grasp success rate on objects from the YCB dataset.
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.