LEGO: Latent-space Exploration for Geometry-aware Optimization of Humanoid Kinematic Design

• URL: http://arxiv.org/abs/2604.08636v1
• Date: Thu, 09 Apr 2026 17:06:50 GMT
• Title: LEGO: Latent-space Exploration for Geometry-aware Optimization of Humanoid Kinematic Design
• Abstract summary: 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.<n>We propose a new paradigm that minimizes human involvement by (i) learning the design search space from existing mechanical designs, and (ii) defining the loss directly from human motion data.<n>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.
• Score: 15.675250550243925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• Function-based Parametric Co-Design Optimization of Dexterous Hands [5.05447792208957]
  We introduce a comprehensive framework for robotic hand generation that unifies palm structure, finger kinematics, fingertip geometry, and fine-scale surface curvatures.<n>We validate the framework on design optimization in grasp stability tasks in simulation and real-world dynamic scenarios.<n>Our framework produces simulation- and fabrication-ready hand models and will be released as open-source.
  arXiv  Detail & Related papers  (2026-04-30T08:06:00Z)
• Advancing Automated Algorithm Design via Evolutionary Stagewise Design with LLMs [69.91297564489464]
  EvoStage is a novel evolutionary paradigm that bridges the gap between the rigorous demands of industrial-scale algorithm design and the LLM-based algorithm design methods.<n>EvoStage decomposes the algorithm design process into sequential, manageable stages and integrates real-time intermediate feedback to iteratively refine algorithm design directions.<n>When deployed on a commercial-grade 3D chip placement tool, EvoStage significantly surpasses the original performance metrics, achieving record-breaking efficiency.
  arXiv  Detail & Related papers  (2026-03-09T05:13:44Z)
• MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction [54.36564144414704]
  MeshMimic is an innovative framework that bridges 3D scene reconstruction and embodied intelligence to enable humanoid robots to learn coupled "motion-terrain" interactions directly from video.<n>By leveraging state-of-the-art 3D vision models, our framework precisely segments and reconstructs both human trajectories and the underlying 3D geometry of terrains and objects.
  arXiv  Detail & Related papers  (2026-02-17T17:09:45Z)
• 3DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimization [53.40777496077015]
  Inverse design aims to design the input variables of a physical system to optimize a specified objective function.<n>In 3D domains, the design space grows exponentially, rendering exhaustive grid-based searches infeasible.<n>We propose a 3D Inverse Design framework that directly navigates the 3D design space by coupling a continuous latent representation with a physics-aware optimization strategy.
  arXiv  Detail & Related papers  (2025-12-06T13:09:03Z)
• Efficient Design of Compliant Mechanisms Using Multi-Objective Optimization [50.24983453990065]
  We address the synthesis of a compliant cross-hinge mechanism capable of large angular strokes.<n>We formulate a multi-objective optimization problem based on kinetostatic performance measures.
  arXiv  Detail & Related papers  (2025-04-23T06:29:10Z)
• A 'MAP' to find high-performing soft robot designs: Traversing complex design spaces using MAP-elites and Topology Optimization [5.774729866385869]
  There are no widely adopted computational design tools that produce quality, manufacturable designs. In this work, we investigate a hierarchical design optimization methodology to generate diverse and high-performance soft robots. Our method provides a new framework to design parts in complex design domains, both soft and rigid.
  arXiv  Detail & Related papers  (2024-07-10T12:27:17Z)
• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• Design Optimizer for Planar Soft-Growing Robot Manipulators [1.1888144645004388]
  This work presents a novel approach for design optimization of soft-growing robots. I optimize the kinematic chain of a soft manipulator to reach targets and avoid unnecessary overuse of material and resources. I tested the proposed method on different tasks to access its optimality, which showed significant performance in solving the problem.
  arXiv  Detail & Related papers  (2023-10-05T08:23:17Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)
• GLSO: Grammar-guided Latent Space Optimization for Sample-efficient Robot Design Automation [16.96128900256427]
  We present Grammar-guided Latent Space Optimization (GLSO), a framework that transforms design automation into a low-dimensional continuous optimization problem. In this work, we present a framework that transforms design automation into a low-dimensional continuous optimization problem by training a graph variational autoencoder (VAE) to learn a mapping between the graph-structured design space and a continuous latent space.
  arXiv  Detail & Related papers  (2022-09-23T17:48:24Z)
• Physical Design using Differentiable Learned Simulators [9.380022457753938]
  In inverse design, learned forward simulators are combined with gradient-based design optimization. This framework produces high-quality designs by propagating through trajectories of hundreds of steps. Our results suggest that despite some remaining challenges, machine learning-based simulators are maturing to the point where they can support general-purpose design optimization.
  arXiv  Detail & Related papers  (2022-02-01T19:56:39Z)
• An End-to-End Differentiable Framework for Contact-Aware Robot Design [37.715596272425316]
  We build an end-to-end differentiable framework for contact-aware robot design. A novel deformation-based parameterization allows for the design of articulated rigid robots with arbitrary, complex geometry. A differentiable rigid body simulator can handle contact-rich scenarios and computes analytical gradients for a full spectrum of kinematic and dynamic parameters.
  arXiv  Detail & Related papers  (2021-07-15T17:53:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.