AI is now evolving the physical skeletons of humanoid robots to move more like us.
The LEGO framework optimizes humanoid joint design by learning a latent space from mechanical parts and human motion data. The robot's physical form is 'grown' to maximize kinematic efficiency rather than being manually engineered.
LEGO: Latent-space Exploration for Geometry-aware Optimization of Humanoid Kinematic Design
arXiv · 2604.08636
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 i