MorphIK: Morphology-Conditioned Neural Inverse Kinematics for Unknown Robots
Neural models can learn to generate various solutions to the inverse kinematics problem from data, but are usually limited to a single robot. We present MorphIK, a flow-matching model that solves inverse kinematics for revolute-joint-based kinematic chains it has never seen during training. The model uses a transformer architecture to encode the robot's morphology along with the target pose. This encoding then conditions a flow-matching head that generates poses from noise. Trained on purely synthetic data from procedurally generated robots, the model reaches a precision of about 5 cm on unseen real-world robots with 6 to 9 Degrees of Freedom. For higher precision, the model serves as an ...
| Authors | Lennart Clasmeier, Jan Gerrit Habekost, Cornelius Weber et al. |
|---|---|
| Primary category | cs.RO |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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