ClothDojo: Learned Data Generation and Benchmarking for Bimanual Garment Flattening and Folding
ClothDojo: Learned Data Generation and Benchmarking for Bimanual Garment Flattening and Folding
This paper introduces ClothDojo, a simulation platform designed for large-scale generation of adaptive manipulation trajectories in bimanual garment flattening and folding tasks.
The authors present ClothDojo, a simulation platform for generating adaptive manipulation trajectories to train policies on bimanual garment flattening and folding tasks.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2611.01190v1 Announce Type: new Abstract: Garment flattening and folding require robots to identify garment structure during manipulation and adapt their actions as the cloth deforms. We present ClothDojo, a simulation platform that generates adaptive manipulation trajectories at scale across diverse garments and initial states and evaluates policies in closed loop. ClothDojo encodes each garment's canonical me
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sh coordinates as an NOCS texture, providing canonical surface-position cues through the visual interface of a pretrained vision-language-action model. We fine-tune the model on human demonstrations and corrections to obtain a data-generation policy. The policy autonomously generates simulated manipulation trajectories, and we retain rollouts that satisfy task-specific success criteria. Each retained trajectory is re
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-rendered under a randomly sampled appearance to provide RGB observations for downstream policies that do not receive NOCS. Across both tasks, the policy produces nearly 8,000 successful trajectories over hundreds of garments. RGB policies trained on these generated trajectories outperform those trained directly on the original human demonstrations and corrections across all evaluated garment splits and appearance se
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ClothDojo: Learned Data Generation and Benchmarking for Bimanual Garment Flattening and Folding
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ttings. Evaluations across multiple policy models show that overall performance and appearance robustness vary across models and tasks, while generalization to unseen garments remains a common challenge.
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Source：arXiv Robotics — research abstracts · arxiv.org