LEGO: Latent-space Exploration for Geometry-aware Optimization of Humanoid Kinematic Design
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917398925279232 |
|---|---|
| author | Yoon, Jihwan Jeong, Taemoon Park, Jeongeun Kim, Chanwoo Kwon, Jaewoon Lee, Yonghyeon Lee, Kyungjae Choi, Sungjoon |
| author_facet | Yoon, Jihwan Jeong, Taemoon Park, Jeongeun Kim, Chanwoo Kwon, Jaewoon Lee, Yonghyeon Lee, Kyungjae Choi, Sungjoon |
| contents | 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 (i) learning the design search space from existing mechanical designs, rather than hand-crafting it, and (ii) defining the loss directly from human motion data via motion retargeting and Procrustes analysis. Using screw-theory-based joint axis representation and isometric manifold learning, we construct a compact, geometry-preserving latent space of humanoid upper body 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08636 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LEGO: Latent-space Exploration for Geometry-aware Optimization of Humanoid Kinematic Design Yoon, Jihwan Jeong, Taemoon Park, Jeongeun Kim, Chanwoo Kwon, Jaewoon Lee, Yonghyeon Lee, Kyungjae Choi, Sungjoon Robotics Artificial Intelligence 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 (i) learning the design search space from existing mechanical designs, rather than hand-crafting it, and (ii) defining the loss directly from human motion data via motion retargeting and Procrustes analysis. Using screw-theory-based joint axis representation and isometric manifold learning, we construct a compact, geometry-preserving latent space of humanoid upper body 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. |
| title | LEGO: Latent-space Exploration for Geometry-aware Optimization of Humanoid Kinematic Design |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2604.08636 |