MorphIK: Morphology-Conditioned Neural Inverse Kinematics for Unknown Robots

2026-09-24

AI Summary

This study addresses the limited generalizability of neural inverse kinematics models to unseen robot morphologies by proposing MorphIK, a framework that parameterizes robot morphology using conditional flow matching and Transformer encoders. To our knowledge, this is the first approach to achieve cross-morphology universal inverse kinematics solving and null-space sampling for serial manipulators. MorphIK generates highly diverse solutions and serves as an efficient prior to accelerate the convergence of traditional optimization methods. Experimental results demonstrate that the model achieves a positioning accuracy of 5 cm on unseen real-world robots. When combined with damped least-squares optimization, the single-step error falls below 1 cm, with most cases reaching sub-millimeter precision within three iterations.

Abstract

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 excellent Prior for further optimization algorithms, reducing error to less than 1 cm after a single step of Damped Least Squares optimization and to sub-1 mm error after 3 steps in most cases. Building on flow matching's generative capabilities to produce highly diverse outputs, our model can efficiently sample the robot's null space, providing a wide variety of configurations for the same pose. Thus, overall, MorphIK allows learning and generalizing neural inverse kinematics for a multitude of known and unknown robots.

Problem

• Inverse Kinematics
• Morphology Generalization
• Unknown Robots
• Neural Inverse Kinematics

Innovation

• Inverse Kinematics
• Flow Matching
• Morphology-Conditioned
• Transformer
• Zero-Shot Generalization