MimicIK: 遠隔操作からのFK整合性を備えたリアルタイム生成的逆運動学
MimicIK: Real-Time Generative Inverse Kinematics from Teleoperation with FK Consistency
遠隔操作デモから条件付きフローマッチングで関節空間の動作事前分布を学習し、現在の関節状態と目標手先姿勢から滑らかでロバストな逆運動学解をリアルタイム生成する手法を提案。FK整合性損失で物理的整合性を確保し、実データで精度・滑らかさ・低遅延を実証。
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著者: Jiahao Yang, Shenhao Yan, Fan Feng, Chengsi Yao, Ge Wang, Zhixin Mai, Yiming Zhao, Yatong Han
分類: cs.RO, cs.AI
原文アブストラクト
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.
