Problem
Research questions and friction points this paper is trying to address.
dynamic rope manipulation
unknown object dynamics
interaction history
RopeFormer: Cross-Trial Adaptation from Interaction History for Dynamic Rope Manipulation
🤖 AI Summary
针对动态绳索操控中未知物体动力学敏感性问题，提出RopeFormer框架，利用历史交互作为后续控制的上下文，无需在线估计绳索参数，提高了操控效果。
📝 Abstract
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 transient whipping, retaining context improves subsequent control relative to resetting the same checkpoint, with the benefit varying across rope dynamics and observation settings. We further deploy the frozen policies on a Unitree H1-2 with previously unseen physical ropes. From T1 to T3, target-acquisition time decreases by 30.9% for Rope Swing and 33.9% for Rope Twirl, while mean Rope Whip target hits increase from 0.2 to 2.3 out of three. These results show that prior interaction can provide effective control context for dynamic deformable-object manipulation. Robot videos, code, and data are available at https://ropeformer.github.io/.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cross-Trial Adaptation
History-Conditioned Framework
Dynamic Rope Manipulation
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