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
Yunsheng Tian
MIT
Jie Xu
Massachusetts Institute of Technology
Yichen Li
Massachusetts Institute of Technology
Shinjiro Sueda
Texas A&M University
Wojciech Matusik
MIT
Related Resources
2023
Language Model Crossover: Variation through Few-Shot Prompting
Pursuing the insight that language models naturally enable an…
2023
SLiMe: Segment Like Me
We explore leveraging extensive vision-language models for segmenting…
2023
What’s In A Name? Evaluating Assembly-Part Semantic Knowledge in Language Models through User-Provided Names in CAD Files
The natural language names designers use in CAD software are a…
2020
Memory-Based Graph Networks
Graph 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