发表机构
Virginia Tech; University of Michigan（弗吉尼亚理工大学; 密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出摇一摇学习策略，利用折纸软体机械臂作为物理储层计算机，通过固定摇晃激励编码隐藏物体质心等属性，并用线性读出解码，完成质心推断与重新抓取任务。
AI 中文摘要
许多与机器人操作相关的物理属性是视觉无法观察到的。例如，一个密封物体在被提起、摇晃或以其他方式动态扰动之前，可能几乎不会透露其质心（COM）或内部内容。本研究表明，此类交互可以启用一种新的机器人感知和学习模式，其中利用交互引起的动态响应来推断传统传感无法获取的物体物理属性。我们使用一个受折纸启发的软体机器人手臂来实现这一想法，该手臂充当物理储层计算机。在抓取物体后，手臂在其基部受到固定的摇晃输入激励，并通过相机跟踪或嵌入式传感器记录由此产生的振铃响应。由于输入在试验间保持不变，隐藏的物体属性（如质心位置）通过它们对耦合机器人-物体系统动力学的影响而被编码。然后，一个轻量级线性读出器可以解码这些动力学，以恢复关于隐藏物体物理属性的可解释信息。使用该框架，软体机器人手臂储层完成了三项难度递增的任务：推断物体隐藏质心的方向，推断质心距抓取点的距离，以及利用推断出的质心信息指导后续的重新抓取。我们进一步开发了振铃响应的动态摘要表示，以提高预测准确性。总之，这些结果确立了“摇一摇学习”机械询问作为一种有前景的策略，使机器人系统能够将短暂的物理交互转化为关于隐藏物体属性的可操作线索，用于下游操作。
英文摘要
Many physical properties relevant to robotic manipulation are hidden from vision. A sealed object, for example, may reveal little about its center of mass (COM) or internal contents until it is lifted, shaken, or otherwise dynamically perturbed. This study shows that such interactions can enable a new modality of robotic perception and learning, in which interaction-induced dynamic responses are used to infer object physics that is inaccessible to conventional sensing. We implement this idea using an origami-inspired soft robotic arm that functions as a physical reservoir computer. After grasping an object, the arm is excited by a fixed shaking input at its base, and the resulting ringdown response is recorded through either camera tracking or embedded sensors. Because the input is held constant across trials, hidden object properties, such as the COM position, are encoded through their effect on the dynamics of the coupled robot-object system. A lightweight linear readout can then decode these dynamics to recover interpretable information about the hidden object physics. Using this framework, the soft robotic arm reservoir completed three tasks of increasing difficulty: inferring the orientation of the object's hidden COM, inferring the COM distance from the grasp point, and using the inferred COM information to guide a subsequent regrasp. We further develop a dynamic summary representation of the ringdown response that improves prediction accuracy. Together, these results establish shake-to-learn mechanical interrogation as a promising strategy for robotic systems to convert brief physical interactions into actionable cues about hidden object properties for downstream manipulation.
Comments17 pages, 6 figures
