全方位適応指による再構成可能な把持学習設計
Reconfigurable Design for Omni-adaptive Grasp Learning
全方位に適応する軟らかい指を再配置できるグリッパ設計を提案し、学習ベース手法で各配置を評価した結果、3本・4本の放射状配置が平均96%の把持成功率を達成した。
著者: Fang Wan, Haokun Wang, Jiyuan Wu, Yujia Liu, Sheng Ge, Chaoyang Song
分類: cs.RO, cs.LG
原文アブストラクト
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.
