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
2025
MSEval: A Dataset for Material Selection in Conceptual Design to Evaluate Algorithmic ModelsThis research explores MSEval, a novel dataset comprised expert…
2025
Aligning Constraint Generation with Design Intent in Parametric CADAn AI method that aligns automatic constraint generation with a…
2023
Sketch-A-Shape: Zero-Shot Sketch-to-3D Shape GenerationGenerative model that can synthesize consistent 3D shapes from a…
2021
Neural UpFlow: A Scene Flow Learning Approach to Increase the Apparent Resolution of Particle-Based LiquidsIn this research, we introduce a data-driven approach to increase 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