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 publicationAbstract
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
Jieliang (Rodger) Luo
Sr. Principal AI Research Scientist
Yunsheng Tian
Massachusetts Institute of Technology
Jie Xu
Massachusetts Institute of Technology
Yichen Li
Massachusetts Institute of Technology
Shinjiro Sueda
Texas A&M University
Wojciech Matusik
MIT
Related Resources
2024
GraphSeam: Supervised Graph Learning Framework for Semantic UV MappingProposing a fully automated UV mapping framework that enables users to…
2023
Conceptual Design Generation Using Large Language ModelsGenerating design concepts in product design using Large Language…
2022
UNIST: Unpaired Neural Implicit Shape Translation NetworkWe introduce UNIST, the first deep neural implicit modelfor…
2020
Memory-Based Graph NetworksGraph neural networks (GNNs) are a class of deep models that operate…
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