Accurate Kinematic Modeling using Autoencoders on Differentiable Joints

In robotics and biomechanics, accurately determining joint parameters and computing the corresponding forward and inverse kinematics are critical yet often challenging tasks, especially when dealing with highly individualized and partly unknown systems. This paper unveils a cutting-edge kinematic optimizer, underpinned by an autoencoder-based architecture, to address these challenges. Utilizing a neural network, our approach simulates inverse kinematics, converting measurement data into joint-specific parameters during encoding, enabling a stable optimization process. These parameters are subsequently processed through a predefined, differentiable forward kinematics model, resulting in a decoded representation of the original data. Beyond offering a comprehensive solution to kinematics challenges, our method also unveils previously unidentified joint parameters. Real experimental data from knee and hand joints validate the optimizer's efficacy. Additionally, our optimizer is multifunctional: it streamlines the modeling and automation of kinematics and enables a nuanced evaluation of diverse modeling techniques. By assessing the differences in reconstruction losses, we illuminate the merits of each approach. Collectively, this preliminary study signifies advancements in kinematic optimization, with potential applications spanning both biomechanics and robotics.