Inverse kinematics (IK) remains a critical bottleneck for real-time robot manipulation. Classical numerical solvers achieve high geometric precision but often suffer from discontinuous branch switching and unstable behavior near kinematic singularities during closed-loop deployment. Meanwhile, learned IK approaches frequently struggle to balance spatial accuracy, motion smoothness, and real-time efficiency, particularly when trained on noisy human teleoperation data. We present \textbf{MimicIK}, a real-time generative inverse kinematics framework that learns smooth and robust joint-space motion priors from teleoperation demonstrations through conditional flow matching. Given the current joint configuration and a target end-effector pose, MimicIK predicts continuous delta-joint commands using an efficient two-step iterative refinement process based on a Minimal Iterative Policy (MIP) backbone. To enforce physical consistency, we further introduce an FK consistency loss, a differentiable forward-kinematics regularization that penalizes task-space deviations from the target pose during training. We evaluate MimicIK on a real-world 6-DOF robot dataset containing 8,848 teleoperation demonstrations. MimicIK achieves a mean position error of 4.65 mm, a 10 mm success rate of 92.01\%, and a trajectory spike rate of only 7.99\%. Compared with a UNet diffusion baseline, our method improves both spatial accuracy and motion smoothness while reducing inference latency from 21.66 ms to 6.74 ms. Furthermore, unlike deterministic MLP baselines that catastrophically diverge under out-of-distribution deployment, MimicIK remains stable near singular configurations and enables robust 20 Hz real-time control on deployment hardware.

翻译：逆向运动学（IK）仍是实时机器人操作的关键瓶颈。经典数值求解器虽能实现高几何精度，但在闭环部署中常面临运动学奇异点附近的连续分支切换及不稳定性问题。与此同时，基于学习的IK方法难以兼顾空间精度、运动平滑性与实时效率，尤其在处理含噪声的人类遥操作数据时。我们提出\textbf{MimicIK}——一种实时生成式逆向运动学框架，通过条件流匹配从遥操作演示中学习平滑鲁棒的关节空间运动先验。给定当前关节构型与目标末端执行器位姿，MimicIK基于最小迭代策略（MIP）主干网络，采用高效的两步迭代优化过程预测连续增量关节指令。为强化物理一致性，我们进一步引入正向运动学一致性损失——一种可微的正向运动学正则化项，用于在训练中惩罚目标位姿在任务空间中的偏差。我们在含8,848组遥操作演示的真实6自由度机器人数据集上评估MimicIK，其平均位置误差为4.65毫米，10毫米成功率高达92.01%，轨迹尖峰率仅为7.99%。与UNet扩散基线模型相比，本方法在提升空间精度与运动平滑性的同时，将推理延迟从21.66毫秒降至6.74毫秒。此外，与确定性MLP基线的灾难性发散不同，MimicIK在奇异构型附近保持稳定，并在部署硬件上实现20赫兹的鲁棒实时控制。