LEGO: Latent-space Exploration for Geometry-aware Optimization of Humanoid Kinematic Design
Summary: arXiv:2604.08636v1 Announce Type: cross
Abstract
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
- (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.
Introduction
Historically, designing humanoid robots has been a tedious process heavily reliant on human expertise and intuition. The emergence of motion-design co-optimization has presented an opportunity to automate aspects of this process, yet significant hurdles remain. The first challenge is the sheer volume of the design space; with countless possible configurations, navigating this terrain can be overwhelming. The second challenge lies in the complexity of creating loss functions that accurately reflect task-specific objectives.
Proposed Methodology
Our approach introduces a systematic method to tackle these challenges. By leveraging existing mechanical designs, we can learn the design search space instead of manually crafting it. This data-driven strategy allows us to build a foundation that is more reflective of practical applications.
Furthermore, we define the loss functions based on human motion data through a combination of motion retargeting and Procrustes analysis. This ensures that the optimization process aligns closely with natural human movements, thereby enhancing the humanoid designs generated by our framework.
Latent Space Construction
Utilizing screw-theory-based joint axis representation and isometric manifold learning, we construct a compact, geometry-preserving latent space specifically for humanoid upper body designs. This latent space serves as a manageable domain for optimization, where the intricacies of the design space are effectively compressed without losing essential geometric information.
Optimization Techniques
To find optimal designs within this latent space, we employ gradient-free optimization techniques. These methods are particularly beneficial in high-dimensional spaces where traditional gradient-based methods may struggle. Our results demonstrate that this innovative approach not only simplifies the design process but also produces novel and functional humanoid designs that adhere to human motion principles.
Conclusion
In conclusion, our framework establishes a principled and data-driven approach to robot design, highlighting the potential of latent-space exploration for geometry-aware optimization. By minimizing human involvement and utilizing existing designs alongside human motion data, we pave the way for an automated discovery of novel robot designs. This research not only contributes to the field of robotics but also opens avenues for further exploration into automated design processes.