发表机构
DGIST; Manning College of Information and Computer Sciences, University of Massachusetts Amherst（大邱庆北科学技术院; 马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出S3N方法（含S3N-Act与S3N-Full），解决平行连杆腿部机构的仿真到现实动力学差距问题，实验显示其可显著降低关节、力等指标的误差，提升仿真到现实一致性。
AI 中文摘要
本文针对平行连杆机构在仿真中用串联树代理表示时产生的动力学仿真到现实差距问题展开研究。传统基于雅可比矩阵的状态和力矩映射虽能保持与运动学及虚功关系的一致性，但未考虑坐标诱导的执行器惯性与阻尼的重新分布，以及串联树简化过程中遗漏的连杆惯性。为解决该差距，本文提出仿真器侧系统归一化（Simulator-Side System Normalization, S3N），在保留串联树拓扑结构的同时对串联树仿真器的有效动力学进行归一化。其中S3N-Act通过坐标变换将执行器惯性与阻尼纳入串联坐标动力学，S3N-Full则通过分别识别执行器级与腿部级的频率响应来恢复残余连杆惯性。在2自由度验证中，与雅可比映射基线相比，S3N-Full使关节位置与力矩的均方根误差（RMSE）分别降低80.9%和82.1%；在原地俯仰运动中，S3N-Act和S3N-Full使地面反作用力范数的RMSE分别降低65.1%和62.4%；在圆周运动中，S3N-Full使相位平均、指令归一化的仿真到现实差距从17.3%降至9.9%。这些结果表明，仿真器侧归一化可提升运动与力层面的仿真到现实一致性，支持在串联树框架中训练与硬件动力学一致的策略，以更好地表征物理平行连杆腿部机构。
英文摘要
This paper addresses the sim-to-real gap in dynamics arising when a parallel-link mechanism is represented by a serial-tree surrogate in simulation. Conventional Jacobian-based state and torque mappings preserve consistency with the kinematic and virtual-work relations but do not account for the coordinate-induced redistribution of actuator inertia and damping and the linkage inertia omitted during serial-tree reduction. To address this gap, Simulator-Side System Normalization (S3N) is proposed to normalize the serial-tree simulator's effective dynamics while preserving its tree topology. S3N-Act incorporates actuator inertia and damping into the serial-coordinate dynamics through coordinate transformation, whereas S3N-Full restores residual linkage inertia by separately identifying actuator- and leg-level frequency responses. In the 2-DoF validation, S3N-Full reduced the joint-position and torque RMSEs by 80.9% and 82.1%, respectively, relative to the Jacobian-mapping baseline. During pitch-in-place motion, S3N-Act and S3N-Full reduced the RMSE of the ground reaction force norm by 65.1% and 62.4%, respectively. During circular locomotion, S3N-Full reduced the phase-averaged, command-normalized sim-to-real gap from 17.3% to 9.9%. These results show that simulator-side normalization improves motion- and force-level sim-to-real consistency. It enables policy training in a serial-tree framework with hardware-consistent dynamics that better represent the physical parallel-link mechanism.
Comments10 pages, 12 figures
