RESEARCH PAPER

FolDeX: A Physical-World Benchmark for Long-Horizon Robotic Manipulation of Deformable Objects

Chenhuan Liu; Yi Xu; Feng Wu; Hanyang Wang; Wenxiao Kuai; Weihao Ding; Shan Wang; Yang Liu; Shuyong Gao; Wenqiang Zhang

Classification

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Architecture
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Prediction paradigm
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Source review status
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Category review. The primary contribution is a physical garment-manipulation benchmark, recovery/transfer tracks and controlled success/quality evaluation; its reference policy does not turn the benchmark into a WAM. The cataloged contribution is a dataset/data-generation method or evaluation/simulation resource, not the architecture of an evaluated or external policy. Reading evidence

AT A GLANCE

Contribution

FolDeX evaluates complete physical garment-folding episodes and asks whether expensive robot experience can be reused across recovery states, tasks, scenes, and embodiments. Its reference policies use π0. Recovery-augmented training raises reported average success from 80.75% to 95.00%, but transfer studies remain preliminary and the missing quality-scoring appendix prevents independent reconstruction of FoldScore from the supplied paper alone.

Abstract

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Affiliations

Fudan University; AI Research Center, Midea Group (Shanghai) Co., Ltd.; Carnegie Mellon University