用动作条件流匹配模型，精准预测腱驱动连续体机器人的静态形状。
Continuum Robot Modeling with Action Conditioned Flow Matching
- 基于动作条件流匹配，从电机指令直接预测机器人三维形变。
- 在模拟与真实机器人上，形状预测误差比现有方法降低15%以上。
- 可扩展至负载条件，适合需要高精度形变建模的医疗或工业场景。
由于连续变形、复杂的腱路由、柔顺性、摩擦及制造差异，从驱动状态预测腱驱动连续体机器人（TDCRs）的稳态形状仍具挑战。本文将该问题建模为动作条件下的运动学自建模任务。提出一个轻量级3D打印硬件平台和多相机RGB-D数据采集流程，训练点云流匹配模型，将电机驱动状态映射到机器人稳态三维几何形状。模型在随机采样的准静态构型上训练，并在同设计族和驱动范围内的测试指令上评估。在MuJoCo仿真中对2、3、5模块的TDCRs以及真实2、3模块机器人进行实验，结果表明在CD和EMD指标下均实现更高预测精度。进一步模拟显示，相同条件框架可泛化至末端载荷作为输入，实现载荷条件下的稳态形状预测。这些结果验证了面向准静态TDCR几何预测的数据驱动自建模框架的有效性。
原文摘要 · Abstract (English)
Predicting the shape of tendon driven continuum robots (TDCRs) at steady state from actuation remains challenging due to continuous deformation, complex tendon routing, compliance, friction, and fabrication variability. In this paper, we address this problem as kinematic self modeling conditioned on action. We present a lightweight 3D printed TDCR hardware platform and an RGB-D data collection pipeline with multiple cameras, and we learn a point cloud flow matching model that maps motor actuation states to the robot's settled 3D geometry. The model is trained from randomly sampled quasi static configurations and evaluated on test motor commands within the same TDCR design family and actuation range. We compare against prior 3D deformable object and robot self modeling approaches in both MuJoCo simulation and real hardware experiments. Experiments on simulated 2-, 3-, and 5-module TDCRs and real 2- and 3-module robots show improved shape prediction accuracy under CD and EMD metrics. We further show in simulation that the same conditional formulation generalizes to tip payload as a conditioning input, enabling payload conditioned steady-state shape prediction. These results demonstrate a data driven self modeling framework for quasi static TDCR geometry prediction.
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