RopeFormer：基于交互历史的跨试验自适应动态绳索操控
RopeFormer: Cross-Trial Adaptation from Interaction History for Dynamic Rope Manipulation
- University of California, Berkeley（加州大学伯克利分校）
- Southern University of Science and Technology（南方科技大学）
- Peking University（北京大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
RopeFormer利用历史交互作为上下文，在无需在线参数估计的情况下，通过保留跨试验历史改善动态绳索操控，仿真和实物实验均验证其有效性。
中文摘要 AI 辅助
动态绳索操控对未知物体动力学高度敏感：相同的机器人运动在不同绳索上可能产生显著不同的响应，而明确识别相关物理属性则较为困难。我们提出RopeFormer，一种基于历史条件的框架，利用先前的任务交互作为后续控制的上下文。该策略在保持权重固定的同时，保留跨试验的动作-响应历史，且无需显式的在线绳索参数估计。在持续单臂旋转、双臂旋转和瞬态鞭打等匹配仿真评估中，保留上下文相对于重置同一检查点能改善后续控制，其收益随绳索动力学和观测设置而变化。我们进一步将冻结策略部署到Unitree H1-2上，使用先前未见过的物理绳索。从T1到T3，Rope Swing的目标获取时间减少30.9%，Rope Twirl减少33.9%，而Rope Whip的平均目标命中次数从三次中的0.2次增加到2.3次。这些结果表明，先前的交互可以为动态可变形物体操控提供有效的控制上下文。机器人视频、代码和数据可在该https URL获取。
英文摘要
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/.
