多模态非抓取式物理参数估计：基于按压-拉动倾翻的方法
Multi-Modal Non-Prehensile Estimation of Physical Parameters via Press-and-Pull Tipping
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中文总结 AI 辅助
该研究提出一种结合滑动与按压-拉动倾翻的多模态框架，通过融合力觉、视觉和本体感觉，无需抓取或先验模型即可估计物体质量、质心高度和摩擦，实验验证了其有效性和鲁棒性。
中文摘要 AI 辅助
通过非抓取式交互恢复未知物体的物理属性具有挑战性，因为单一操作原语无法揭示所有相关参数。平面推动将质量与摩擦耦合，而传统倾翻无法恢复摩擦，并且在低摩擦或曲面底部物体发生滑动或旋转而非倾翻时可能完全失效。我们提出了一种多模态估计框架，结合滑动交互与按压-拉动倾翻原语，以恢复物体质量、质心高度和表面摩擦。按压-拉动交互提高了物体-桌面滑动阈值并稳定了枢轴，从而在无需任何先验几何物体模型的情况下实现受控倾翻。腕部力/力矩传感、RGB-D感知和机器人本体感觉被融合，以从两种互补的交互模式中估计物理参数。在ABB IRB120上对四种具有不同几何形状、质量、质心和摩擦的物体进行的实验实现了低相对误差，同时成功处理了在传统前向倾翻下失效的曲面底部物体。结果表明，互补的非抓取式交互可以在无需抓取、先验物体模型或学习交互动力学的情况下恢复一组紧凑的物理参数。
英文摘要
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.
