Computational Design of Coordinate-Motion Assemblies
Yukun Lu, Ke Chen, Ligang Liu, Peng Song
Abstract
Coordinate-motion assemblies can only be disassembled by the simultaneous motion of multiple parts along distinct paths, providing high structural stability and enabling efficient robotic assembly. Existing examples are largely limited to architectural structures using joint-based connections, or puzzles created through trial-and-error, as the relationship between part geometry and coordinate motion remains poorly understood. Computationally designing such assemblies is challenging because it requires jointly achieving distributed contacts across the entire assembly and a unique coordinate motion for disassembly that rules out other feasible motions. We address this challenge by establishing a theoretical connection between part geometry and unique coordinate motion, enabling us to rigorously verify whether a given assembly admits a unique coordinate motion. Building on this theory, we introduce a two-stage algorithm that optimizes contact interfaces for a target motion and constructs physically feasible part geometries that conform to a user-specified global shape. We demonstrate our approach on models with complex geometries and topologies, including assemblies with large part counts, and validate it through physical fabrication and experiments.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get 3b575ea9-8c77-4fe8-a8e9-abf1bd079ee6Related papers
- Computational design of high-level interlocking puzzlesRulin Chen, Ziqi Wang, Peng Song, Bernd BickelSIGGRAPH 2022 · 25 citations
- MOCCA: modeling and optimizing cone-joints for complex assembliesZiqi Wang, Peng Song, Mark PaulySIGGRAPH 2021 · 10 citations
- Category-Level Multi-Part Multi-Joint 3D Shape AssemblyYichen Li, Kaichun Mo, Yueqi Duan, He Wang et al.CVPR 2024
- Neural Assembler: Learning to Generate Fine-Grained Robotic Assembly Instructions from Multi-View ImagesHongyu Yan, Yadong MuAAAI 2025 · 3 citations
- Leveraging SE(3) Equivariance for Learning 3D Geometric Shape AssemblyRuihai Wu, Chenrui Tie, Yushi Du, Yan Zhao et al.ICCV 2023 · 34 citations
