MorphIK neural model solves inverse kinematics for unknown robots
MorphIK: Morphology-Conditioned Neural Inverse Kinematics for Unknown Robots
RoboticsArtificial Intelligence
Summary
Controlling a robot’s arm to reach a certain position involves solving a complex math problem called inverse kinematics. Usually, programs that solve this problem are made for one specific robot. The authors created MorphIK, a neural network that can understand different robot shapes and find arm configurations even for robots it has never seen before. It first gives a rough answer and then a quick optimization makes the position very precise. This also allows the system to suggest many ways the robot arm can move to reach the same spot.
What this means in practice
- •For robotics engineers: Generate arm poses for new robot designs without retraining inverse kinematics models manually.
- •For animation artists: Create diverse, physically realistic robot arm postures for unknown robot models in animated scenes.
Authors
Lennart Clasmeier, Jan Gerrit Habekost, Cornelius Weber, Stefan Wermter
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.