发表机构
Worcester Polytechnic Institute（伍斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种力引导的主动感知方法，通过单次亚临界倾翻实验估计未知物体的三维质心，无需形状先验，实验误差低于5%，支持非抓取操作。
AI 中文摘要
当抓取不可行、几何形状不规则或质量分布不均匀时，估计未知物体的三维质心具有挑战性。我们提出了一种基于力的方法，通过机器人操作器进行的单次亚临界倾翻实验来估计质心高度和质量。机器人施加准静态的提升推挤和回缩运动，利用倾翻过程中记录的力-角度测量值，从物体轨迹中识别参数。我们提出的推挤-回缩循环减轻了摩擦偏差，实现了广义拟合。我们使用带有六轴力/力矩传感器的机器人操作器，在无需先验形状信息且无需特定模型的情况下，对不同类型的物体进行了实验验证。我们还提出了一种防止翻倒的方法，通过利用安全裕度使物体保持在亚临界倾翻状态。在实验研究中，我们的方法在所有未知物体上恢复质量、质心高度和倾翻角度的相对误差均低于5.0%。这项工作展示了在适当的倾翻安全阈值下可靠的3D惯性参数估计。我们提出的方法为以前不可行的具有挑战性物体的可靠非抓取操作和机器人抓取提供了信息并使其成为可能。
英文摘要
Estimating the 3D center of mass of unknown objects is challenging when grasping is infeasible, geometry is irregular, or mass distribution is uneven. We present a force-based method that estimates CoM height and mass from a single sub-critical tipping experiment by a robot manipulator. The robot applies a quasistatic elevated push and retract motion, using force-angle measurements recorded during tipping to identify parameters from the object trajectory. Our proposed push-retract cycle mitigates frictional bias, enabling generalized fitting. We experimentally validate our method using a robot manipulator with a six-axis force torque sensor on varying types of objects without prior shape information and without specific models. We also propose a method to prevent toppling, keeping the object in a sub-critical tipping regime by leveraging a safety margin. In experimental studies, our method recovers mass, CoM height, and toppling angle with relative errors below 5.0 percent across all unknown objects. This work demonstrates reliable 3D inertial parameter estimation under proper safety thresholds in tipping. Our proposed method informs and enables reliable non-prehensile manipulation and robotic grasping of challenging objects that were previously infeasible.
CommentsFor associated video, visit IROS2026.mp4" target="_blank" rel="noopener">https://stevenmhyland.com/assets/videos/IROS2026.mp4
