Scalable Surface-Based Manipulation Through Modularity and Inter-Module Object Transfer

TL;DR

This paper introduces a scalable, modular soft manipulation platform that enables reliable object transfer and precise positioning across interconnected fabric modules, reducing actuator count and addressing interference issues.

Contribution

It presents a hierarchical control framework and shared-boundary actuation design that significantly improves scalability and transfer reliability in soft manipulation surfaces.

Findings

Achieves sub-centimeter positioning accuracy.

Reduces actuator count from 4n^2 to (n+1)^2 in an n x n grid.

Compensation strategies decrease passive-object displacement by up to 78%.

Abstract

Robotic Manipulation Surfaces (RMS) manipulate objects by deforming the surface on which they rest, offering safe, parallel handling of diverse and fragile items. However, existing designs face a fundamental tradeoff: achieving fine control typically demands dense actuator arrays that limit scalability. Modular architectures can extend the workspace, but transferring objects reliably across module boundaries on soft, continuously deformable surfaces remains an open challenge. We present a multi-modular soft manipulation platform that achieves coordinated inter-module object transfer and precise positioning across interconnected fabric-based modules. A hierarchical control framework, combining conflict-free Manhattan-based path planning with directional object passing and a geometric PID controller, achieves sub-centimeter positioning and consistent transfer of heterogeneous objects including fragile items. The platform employs shared-boundary actuation, where adjacent modules share edge actuators, reducing the required count from $4n^2$ to $(n + 1)^2$ for an $n \times n$ grid; a $2\times 2$ prototype covers $1\times 1$ m with only 9 actuators. This scaling comes at a cost: shared actuators mechanically couple neighbouring modules, creating interference during simultaneous manipulation. We systematically characterise this coupling across spatial configurations and propose compensation strategies that reduce passive-object displacement by 59--78\%. Together, these contributions establish a scalable foundation for soft manipulation surfaces in applications such as food processing and logistics.