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.

翻译：针对冗余仿人机械臂的传统逆运动学方法通常强调末端执行器轨迹跟踪，其生成的构型虽在机械上有效，却往往缺乏类人特征。本文提出类人逆运动学框架，该轻量化框架在保持末端执行器跟踪能力的同时，能塑造出整体呈现类人姿态的手臂构型，且无需运行时全身感知。其核心思想在于学习肘部运动先验：通过将大规模人体运动数据重定向至机器人，我们训练了一个FiLM调制的时空注意力网络，用于根据末端执行器目标及末端-肘部状态的短期历史预测下一时刻的肘部姿态。该预测结果作为微小残差项，与末端执行器跟踪项和平滑性约束项共同纳入标准的Levenberg-Marquardt优化器中，使得HL-IK能够直接嵌入数值逆运动学求解流程。在超过18.3万次仿真步长中，HL-IK将手臂相似度的位置误差与方向误差平均降低了30.6%和35.4%，在最具挑战性的轨迹上更分别降低了42.2%和47.4%。在仿真平台之外的实体机器人上进行的硬件遥操作实验进一步验证了其在拟人化方面的提升效果。HL-IK具有易于集成、可通过我们的流程跨平台适配、计算开销极低等优势，能为仿人机器人实现类人运动提供有效支持。