プレス・アンド・プル・ティッピングによる物理パラメータのマルチモーダル非把持推定
Multi-Modal Non-Prehensile Estimation of Physical Parameters via Press-and-Pull Tipping
把持せずに物体を押し引きして傾け、力覚・RGB-D・固有感覚を融合して質量・重心高さ・摩擦を推定する手法を提案。従来の前方ティッピングが失敗する曲面ベース物体でも高精度に推定できる。
著者: Steven M. Hyland, Jing Xiao, Cagdas D. Onal
分類: cs.RO
原文アブストラクト
Recovering physical properties of unknown objects through non-prehensile interaction is challenging because no single manipulation primitive reveals all relevant parameters. Planar pushing couples mass and friction, while conventional tipping cannot recover friction and may fail entirely when low-friction or curved-base objects slide or rotate instead of tipping. We introduce a multi-modal estimation framework that combines a sliding interaction with a press-and-pull tipping primitive to recover object mass, center of mass height, and surface friction. The press-and-pull interaction increases the object-table sliding threshold and stabilizes the pivot, enabling controlled tipping without any prior geometric object model. Wrist force/torque sensing, RGB-D perception, and robot proprioception are fused to estimate the physical parameters from the two complementary interaction modes. Experiments on an ABB IRB120 across four objects with varied geometry, mass, center of mass, and friction achieve low relative error, while successfully operating on curved-base objects that fail under conventional forward tipping. The results demonstrate that complementary non-prehensile interactions can recover a compact set of physical parameters without grasping, a prior object model, or learned interaction dynamics.