Designing robot morphologies and kinematics has traditionally relied on human intuition, with little systematic foundation. Motion–design co-optimization offers a promising path toward automation, but two major challenges remain: (i) the vast, unstructured design space and (ii) the difficulty of constructing task-specific loss functions.
We propose a new paradigm that minimizes human involvement by:
Using screw-theory-based joint axis representation and isometric manifold learning, we construct a compact, geometry-preserving latent space of robot designs in which optimization is tractable. We then solve design optimization in this latent space using gradient-free optimization. Our approach establishes a principled framework for data-driven robot design and demonstrates that leveraging existing designs and human motion can effectively guide the automated discovery of novel robot design.
Our framework models each joint as a 6D vector S=(ω,q) , where ω is the unit axis and q is the global position , enabling a unified and globally consistent description of diverse humanoid morphologies that preserves explicit positional information for motion retargeting.
g1
hrp4
igus_op
Explore and manipulate the generated robot models by our method.
@article{yoon2026LEGO,
title={LEGO: Latent‑space Exploration for Geometry‑aware Optimization of Humanoid Kinematic Design},
author={JiHwan Yoon, Taemoon Jeong, Jeongeun Park, Chanwoo Kim,
Jaewoon Kwon, Yonghyeon Lee, Kyungjae Lee, Sungjoon Choi},
journal={arxiv(will be uploaded)},
year={2026}
}