把持・滑り・転がり：触覚による形状復元のための接触モード比較分析
Grasp, Slide, Roll: Comparative Analysis of Contact Modes for Tactile-Based Shape Reconstruction
触覚を備えた多指ハンドで物体を把持・滑らせ・転がす3つの接触モードを比較し、情報理論的な探索と形状補完モデルを組み合わせることで、より少ない接触回数で高精度な形状復元を実現した。
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著者: Chung Hee Kim, Shivani Kamtikar, Tye Brady, Taskin Padir, Joshua Migdal
分類: cs.RO
原文アブストラクト
Tactile sensing allows robots to gather detailed geometric information about objects through physical interaction, complementing vision-based approaches. However, efficiently acquiring useful tactile data remains challenging due to the time-consuming nature of physical contact and the need to strategically choose contact locations that maximize information gain while minimizing physical interactions. This paper studies how different contact modes affect object shape reconstruction using a tactile-enabled dexterous gripper. We compare three contact interaction modes: grasp-releasing, sliding induced by finger-grazing, and palm-rolling. These contact modes are combined with an information-theoretic exploration framework that guides subsequent sampling locations using a shape completion model. Our results show that the improved tactile sensing efficiency of finger-grazing and palm-rolling translates into faster convergence in shape reconstruction, requiring 34% fewer physical interactions while improving reconstruction accuracy by 55%. We validate our approach using a UR5e robot arm equipped with an Inspire-Robots Dexterous Hand, showing robust performance across primitive object geometries.
