# ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility

arXiv: https://arxiv.org/abs/2309.16909 · HTML: https://mlsanity.com/paper/2309.16909/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints](https://mlsanity.com/paper/2111.12772/) (2021)
- [An End-to-End Differentiable Framework for Contact-Aware Robot Design](https://mlsanity.com/paper/2107.07501/) (2021)
- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [Learning 3D Part Assembly from a Single Image](https://mlsanity.com/paper/2003.09754/) (2020)
- [Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly](https://mlsanity.com/paper/1910.13675/) (2019)
- [RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment](https://mlsanity.com/paper/2112.10143/) (2021)
- [Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data](https://mlsanity.com/paper/1902.09157/) (2019)
- [A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly](https://mlsanity.com/paper/2010.08052/) (2020)
- [Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery](https://mlsanity.com/paper/2203.04120/) (2022)
- [Robotic additive construction of bar structures: Unified sequence and motion planning](https://mlsanity.com/paper/2105.11438/) (2021)
- [AssembleRL: Learning to Assemble Furniture from Their Point Clouds](https://mlsanity.com/paper/2209.07268/) (2022)

## Built on by (most cited first, 18 of 18)

- [From Imitation to Refinement -- Residual RL for Precise Assembly](https://mlsanity.com/paper/2407.16677/) (2024)
- [JUICER: Data-Efficient Imitation Learning for Robotic Assembly](https://mlsanity.com/paper/2404.03729/) (2024)
- [PhysVLM: Enabling Visual Language Models to Understand Robotic Physical Reachability](https://mlsanity.com/paper/2503.08481/) (2025)
- [Blox-Net: Generative Design-for-Robot-Assembly Using VLM Supervision, Physics Simulation, and a Robot with Reset](https://mlsanity.com/paper/2409.17126/) (2024)
- [Manual2Skill: Learning to Read Manuals and Acquire Robotic Skills for Furniture Assembly Using Vision-Language Models](https://mlsanity.com/paper/2502.10090/) (2025)
- [SPAFormer: Sequential 3D Part Assembly with Transformers](https://mlsanity.com/paper/2403.05874/) (2024)
- [Manual2Skill++: Connector-Aware General Robotic Assembly from Instruction Manuals via Vision-Language Models](https://mlsanity.com/paper/2510.16344/) (2025)
- [CADEngBench: It Looks Like CAD, but Does It Work? Evaluating Parametric Design, Assembly Reasoning, and Physics Simulation](https://mlsanity.com/paper/2608.09296/) (2026)
- [Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly](https://mlsanity.com/paper/2409.18390/) (2024)
- [AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects](https://mlsanity.com/paper/2605.12845/) (2026)
- [Subassembly to Full Assembly: Effective Assembly Sequence Planning through Graph-based Reinforcement Learning](https://mlsanity.com/paper/2409.13620/) (2024)
- [Query-Centric Diffusion Policy for Generalizable Robotic Assembly](https://mlsanity.com/paper/2509.18686/) (2025)
- [Optimal Knock-Pick Planning for Tightly Packed Tabletop Blocks With Parallel Grippers](https://mlsanity.com/paper/2605.17800/) (2026)
- [Three-Phase Evaluation of AI-Assisted Software Development Life Cycle](https://mlsanity.com/paper/2607.05125/) (2026)
- [Terminal Symmetry as a Carrier of Asymmetric Process Knowledge: Statewise Refinement for Anytime Verified Construction](https://mlsanity.com/paper/2608.11318/) (2026)
- [PVRA: A Pointwise Key-point Voting Framework for Robotic Assembly](https://mlsanity.com/paper/2608.19968/) (2026)
- [SNAP3D: Physically Grounded 3D Parts for Assembly from a Single Image](https://mlsanity.com/paper/2609.13146/) (2026)
- [ScaffoldM3C: A Multimodal Sequential Monte Carlo Framework for Generative Stable Construction Planning](https://mlsanity.com/paper/2610.00487/) (2026)

## Abstract (arXiv)

The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together. In this paper, we present ASAP, a physics-based planning approach for automatically generating such a sequence for general-shaped assemblies. ASAP accounts for gravity to design a sequence where each sub-assembly is physically stable with a limited number of parts being held and a support surface. We apply efficient tree search algorithms to reduce the combinatorial complexity of determining such an assembly sequence. The search can be guided by either geometric heuristics or graph neural networks trained on data with simulation labels. Finally, we show the superior performance of ASAP at generating physically realistic assembly sequence plans on a large dataset of hundreds of complex product assemblies. We further demonstrate the applicability of ASAP on both simulation and real-world robotic setups. Project website: asap.csail.mit.edu