AI 中文总结
本研究通过膜耦合Delta阵列将离散接触转为连续表面，消除执行器间距对物体尺寸的下限，实现15-90mm物体的操控，策略迁移硬件成功率达76%。
AI 中文摘要
分布式操控系统通过多个执行器的协调运动来操控物体。然而，物体必须同时由多个执行器支撑，因此执行器中心到中心的间距对可操控物体的尺寸施加了硬性下限。我们通过将8×8阵列的三自由度Delta机器人的末端执行器与可拉伸织物耦合，消除了这一限制，将64个离散接触点转变为连续表面，能够操控小于执行器间距的物体。将该阵列视为该表面上的位移场，我们研究了局部准静态和循环场作为操控原语。这些原语可以全局应用于整个阵列，或局部限制在每个被跟踪物体周围，以并行独立操控多个物体。随后，我们训练了一个作用于低阶离散余弦变换系数的策略：在相同动作维度下，指挥十九个Delta邻域可将放置误差减半，相比指挥整个阵列。该策略无需调整即可迁移到硬件，成功率为76%。该平台可在单一表面上操控15毫米至90毫米的物体，覆盖执行器间距43.3毫米两侧的六倍范围。
英文摘要
Distributed manipulator systems manipulate objects through the coordinated motion of many actuators. However, an object must be supported by several actuators at once, so the centre-to-centre actuator spacing imposes a hard lower bound on manipulable object size. We remove this bound by coupling the end-effectors of an 8 x 8 array of three degrees-of-freedom delta robots with a stretchable fabric, turning 64 discrete contacts into a continuous surface capable of manipulating objects smaller than the actuator spacing. Viewing the array as a displacement field over that surface, we investigate local quasi-static and cyclic fields as manipulation primitives. These primitives can be applied globally across the array or locally confined around each tracked object to independently manipulate several objects in parallel. We then train a policy acting on low-order discrete cosine transform coefficients: at equal action dimension, commanding a nineteen-delta neighbourhood halves the placement error of commanding the whole array. The policy transfers to hardware without adaptation at 76% success. The platform manipulates objects from 15mm-90mm, a six-fold range spanning both sides of the actuator spacing, 43.3mm, on a single surface.
