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RTFF: Random-to-Target Fabric Flattening policy
using dual-arm manipulator
File(s)
Author(s)
Bhattacharya, Dipankar
Tang, Kai
Xu, Hang
Tokuda, Fuyuki
Tien, Norman
Type
Conference Paper
Abstract
Robotic fabric manipulation remains challenging due to fabric deformability and occlusions from wrinkles and the manipulator. This paper defines Random-to-Target Fabric Flattening (RTFF) as the task of bringing a randomly wrinkled fabric to an arbitrary user-specified wrinkle-free target pose. RTFF requires simultaneous flattening and pose alignment, where the two objectives are inherently coupled since flattening the fabric displaces its pose, while realigning it tends to introduce wrinkles. To solve this task, this paper anchors both the current and target fabric states to the same template mesh, enabling direct vertex-level wrinkle and pose assessment without registration. Building on this representation, a hybrid Imitation Learning--Visual Servoing (IL--VS) RTFF policy is proposed. A novel Mesh Action Chunking Transformer (MACT) leverages structured mesh observations to achieve goal-conditioned coarse alignment from a compact demonstration set, after which VS ensures precise convergence to the target. The policy is validated on a real dual-arm teleoperation system, demonstrating precise alignment to unseen target poses, fabric types, and scales. Code and videos:
Date Acceptance
2026-06-17
Citation
2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
URI
Publisher
IEEE
Journal / Book Title
2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Copyright Statement
Subject to copyright. This paper is embargoed until publication. Once published the author’s accepted manuscript will be made available under a CC-BY License in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy).
License URL
Identifier
https://kaitang98.github.io/RTFF_Policy/
Source
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publication Status
Accepted
Start Date
2026-09-27
Finish Date
2026-10-01
Coverage Spatial
Pittsburgh, PA, USA