基于历史条件流匹配的腱驱动连续体机器人概率动力学建模
History-Conditioned Flow Matching for Probabilistic Dynamics of Tendon-Driven Continuum Robots
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中文总结 AI 辅助
针对腱驱动连续体机器人动力学建模中的不确定性，提出基于历史条件的物理信息流匹配框架，利用运动与驱动历史预测下一构型分布，在仿真和物理机器人上均优于基线，实现更准确的概率动力学预测。
中文摘要 AI 辅助
由于材料行为、腱传动、摩擦和接触方面的不确定性，腱驱动连续体机器人的确定性动力学建模仍然具有挑战性。测得的关节构型和标称腱命令并不能完全表征这些内部机械因素，从而在后续运动中留下不确定性。因此，我们开发了一种基于历史条件、物理信息驱动的流匹配框架，用于概率动力学预测，利用运动和驱动历史来预测下一个完整关节构型的分布。通过在给定命令下递归采样下一步构型，该模型能够预测未来整体运动的分布。在仿真中，特定场景模型在内部摩擦变化下实现了12.05毫米的五秒轨迹能量分数（越低越好），在未观测的驱动扰动下实现了9.29毫米。相对于条件变分自编码器和扩散基线，Flow在两个场景中都获得了更低的能量分数和更接近标称水平的覆盖率。消融实验支持了历史和结构条件在两个场景中的有效性。在物理机器人上，在两种未包含在训练中的腱命令配置文件下的预测捕捉了主要运动序列，五秒能量分数分别为11.91和11.42毫米，低于比较的基线。预测与测量的扩散比分别为1.65和1.22（越接近1越好）。这些结果支持了在不完整机械观测下基于历史条件的概率动力学预测。
英文摘要
Deterministic dynamics modeling of tendon-driven continuum robots remains challenging owing to uncertainties in material behavior, tendon transmission, friction, and contact. Measured joint configurations and nominal tendon commands do not fully characterize these internal mechanical factors, leaving uncertainty in the subsequent motion. We therefore develop a history-conditioned, physics-informed flow-matching framework for probabilistic dynamics prediction, using motion and actuation histories to predict the distribution of the next complete joint configuration. By recursively sampling next-step configurations under prescribed commands, the model predicts distributions of future whole-body motions. In simulation, scenario-specific models achieve five-second trajectory Energy Scores (lower is better) of 12.05 mm under internal friction variation and 9.29 mm under unobserved actuation disturbances. Relative to the conditional variational autoencoder and diffusion baselines, Flow attains lower Energy Scores and coverage closer to the nominal level in both scenarios. Ablations support history and structural conditioning in both scenarios. On the physical robot, predictions under two tendon-command profiles excluded from training capture the principal motion sequences, with five-second Energy Scores of 11.91 and 11.42 mm, lower than the compared baselines. The predicted-to-measured spread ratios are 1.65 and 1.22 (closer to 1 is better). These results support history-conditioned probabilistic dynamics prediction under incomplete mechanical observations.
