让机器人手臂动作更像人，仅靠少量计算提升自然度。
HL-IK: A Lightweight Implementation of Human-Like Inverse Kinematics in Humanoid Arms
- 用人体运动数据训练网络预测肘部位置，引导机械臂模仿人类姿态。
- 仿真中平均降低30.6%位置误差、35.4%方向误差，难轨迹下提升超40%。
- 无需全身感知，可轻松接入现有系统，适合快速部署的机器人项目。
传统逆运动学方法虽能精准追踪末端执行器（EE），但生成的姿态常不具人形。本文提出轻量级人类姿态逆运动学框架HL-IK，通过学习肘部先验，在保持末端跟踪精度的同时，使整个手臂姿态更接近人类。核心是基于大规模人体运动数据重定向至机器人后训练的FiSTA网络，该网络以末端目标与短时序的末端-肘部状态为输入，预测下一步肘部位置，并将其作为残差项融合进标准Levenberg-Marquardt优化器中。在超过18.3万次仿真步中，HL-IK平均降低臂部相似性位置误差30.6%、方向误差35.4%，在最挑战轨迹上分别降低42.2%和47.4%。硬件遥操作实验进一步验证了其在真实机器人上的拟人化提升。该方法实现简单，可跨平台适配，计算开销极低，适用于各类人形机器人。
原文摘要 · Abstract (English)
Traditional IK methods for redundant humanoid manipulators emphasize end-effector (EE) tracking, frequently producing configurations that are valid mechanically but not human-like. We present Human-Like Inverse Kinematics (HL-IK), a lightweight IK framework that preserves EE tracking while shaping whole-arm configurations to appear human-like, without full-body sensing at runtime. The key idea is a learned elbow prior: using large-scale human motion data retargeted to the robot, we train a FiLM-modulated spatio-temporal attention network (FiSTA) to predict the next-step elbow pose from the EE target and a short history of EE-elbow states.This prediction is incorporated as a small residual alongside EE and smoothness terms in a standard Levenberg-Marquardt optimizer, making HL-IK a drop-in addition to numerical IK stacks. Over 183k simulation steps, HL-IK reduces arm-similarity position and direction error by 30.6% and 35.4% on average, and by 42.2% and 47.4% on the most challenging trajectories. Hardware teleoperation on a robot distinct from simulation further confirms the gains in anthropomorphism. HL-IK is simple to integrate, adaptable across platforms via our pipeline, and adds minimal computation, enabling human-like motions for humanoid robots.
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