Publication | ACM Transactions on Graphics (SIGGRAPH Asia Proceedings) 2022

Assemble Them All

Physics-Based Planning for Generalizable Assembly by Disassembly

Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of dealing with arbitrary 3D shapes and the highly constrained motion required for real-world assemblies. In this work, we propose a novel method to efficiently plan physically plausible assembly motion and sequences for real-world assemblies.

Download publication

Abstract

Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly

Yunsheng Tian, Jie Xu, Yichen Li, Jieliang Luo, Shinjiro Sueda, Hui Li, Karl D.D. Willis, Wojciech Matusik

ACM Transactions on Graphics (SIGGRAPH Asia Proceedings) 2022

Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of dealing with arbitrary 3D shapes and the highly constrained motion required for real-world assemblies. In this work, we propose a novel method to efficiently plan physically plausible assembly motion and sequences for real-world assemblies. Our method leverages the assembly-by-disassembly principle and physics-based simulation to efficiently explore a reduced search space. To evaluate the generality of our method, we define a large-scale dataset consisting of thousands of physically valid industrial assemblies with a variety of assembly motions required. Our experiments on this new benchmark demonstrate we achieve a state-of-the-art success rate and the highest computational efficiency compared to other baseline algorithms. Our method also generalizes to rotational assemblies (e.g., screws and puzzles) and solves 80-part assemblies within several minutes.

Associated Researchers

Yunsheng Tian

MIT

Jie Xu

Massachusetts Institute of Technology

Yichen Li

Massachusetts Institute of Technology

Shinjiro Sueda

Texas A&M University

Wojciech Matusik

MIT

View all researchers

Related Resources

Publication

2021

RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers

The 3D deep learning community has seen significant strides in…

Publication

2022

Neural Implicit Style-Net: synthesizing shapes in a preferred style exploiting self supervision

We introduce a novel approach to disentangle style from content in the…

Publication

2020

PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing

Deep classifiers tend to associate a few discriminative input…

Publication

2021

Inferring CAD Modeling Sequences using Zone Graphs

In computer-aided design (CAD), the ability to “reverse engineer” the…

Get in touch

Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.

Contact us