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

arXiv · AI, language, vision and robotics · article · Sep 9, 2026 · UTC

Embodied AI, including vision-language-action and world-action models, must operate reliably in the physical world. Yet methods that perform well in simulation can degrade substantially on real robots, especially in long-horizon deformable-object manipulation, where policies must track changing states and execute reliable multi-stage bimanual interactions. Existing real-robot benchmarks mainly focus on short-horizon rigid-object tasks and offer limited coverage of long-horizon deformable manipulation. We introduce FolDeX, a physical-world benchmark built entirely from real-robot data, with gar

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