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This research presents a novel robotic manipulation platform that uses a stretchable fabric to couple discrete delta robot actuators, effectively creating a continuous surface capable of handling objects smaller than the spacing between individual units. The system leverages machine learning policies trained on low-order discrete cosine transform coefficients to optimize control signals, significantly reducing placement error while enabling parallel manipulation of multiple objects. By transferring this learned policy directly to hardware with high success rates, the study demonstrates a practical advancement in distributed robotics and adaptive control strategies.
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Making Waves: A Membrane-Coupled Delta Array for Manipulating Objects Below the Actuator Spacing

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arXiv:2609.39652v1 Announce Type: new Abstract: 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.

Abstract: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.

Robotics (cs.RO)

arXiv:2609.39652 [cs.RO]

(or arXiv:2609.39652v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.39652 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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