ROMar 12

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

arXiv:2601.218844.9h-index: 2
Predicted impact top 76% in RO · last 90 daysOriginality Highly original
AI Analysis

This work addresses scalability issues in soft robotic manipulation for applications like food processing and logistics, representing a novel method for a known bottleneck.

The paper tackles the challenge of scalable robotic manipulation surfaces by introducing a multi-modular platform that achieves sub-centimeter positioning and reliable inter-module object transfer, reducing actuator count from 4n^2 to (n+1)^2 for an n×n grid.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes