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：逆运动学仍然是实时机器人操作中的关键瓶颈。经典数值求解器虽能实现高几何精度，但在闭环部署中常出现分支切换不连续以及运动学奇异性附近的失稳行为。同时，基于学习的逆运动学方法在平衡空间精度、运动平滑性和实时效率方面仍面临挑战，尤其是在基于含噪声的人体遥操作数据进行训练时。我们提出MimicIK——一种基于遥操作示教通过条件流匹配学习平滑且鲁棒关节空间运动先验的实时生成式逆运动学框架。给定当前关节构型与目标末端执行器位姿，MimicIK采用以最小迭代策略网络为骨干的高效两步迭代精化流程，预测连续的增量式关节指令。为强制执行物理一致性，我们进一步引入正运动学一致性损失——一种可微的正运动学正则化项，在训练过程中惩罚末端执行器任务空间位姿与目标位姿的偏差。我们在包含8,848条遥操作示教数据的真实6自由度机器人数据集上评估了MimicIK，其平均位置误差为4.65毫米，10毫米成功率92.01%，轨迹尖峰率仅7.99%。相较于UNet扩散基线模型，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，不同于确定性多层感知机基线的分布外部署导致灾难性发散，MimicIK在奇异构型附近仍保持稳定，并在部署硬件上实现鲁棒的20赫兹实时控制。