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RopeFormer: Cross-Trial Adaptation from Interaction History for Dynamic Rope Manipulation

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

Dynamic rope manipulation is highly sensitive to unknown object dynamics: the same robot motion can produce substantially different responses across ropes, while explicitly identifying the relevant physical properties is difficult. We present RopeFormer, a history-conditioned framework that uses prior task interaction as context for subsequent control. The policy retains cross-trial action-response history while keeping its weights fixed and requires no explicit online rope-parameter estimation. In matched simulation evaluations across sustained single-arm rotation, bimanual rotation, and tran

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