# Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

arXiv: https://arxiv.org/abs/2106.02489 · HTML: https://mlsanity.com/paper/2106.02489/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2021.

## Built on by (most cited first, 24 of 24)

- [ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning](https://mlsanity.com/paper/2308.13724/) (2023)
- [Learning Multi-Object Dynamics with Compositional Neural Radiance Fields](https://mlsanity.com/paper/2202.11855/) (2022)
- [PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation Tasks](https://mlsanity.com/paper/2407.00278/) (2024)
- [Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning](https://mlsanity.com/paper/2203.13733/) (2022)
- [DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation](https://mlsanity.com/paper/2207.01971/) (2022)
- [RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment](https://mlsanity.com/paper/2112.10143/) (2021)
- [Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning](https://mlsanity.com/paper/2309.02547/) (2023)
- [Cooperative Task and Motion Planning for Multi-Arm Assembly Systems](https://mlsanity.com/paper/2203.02475/) (2022)
- [Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery](https://mlsanity.com/paper/2203.04120/) (2022)
- [RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2509.05397/) (2025)
- [Constant-time Motion Planning with Anytime Refinement for Manipulation](https://mlsanity.com/paper/2311.00837/) (2023)
- [V-MAO: Generative Modeling for Multi-Arm Manipulation of Articulated Objects](https://mlsanity.com/paper/2111.03987/) (2021)
- [Hypergraph-based Multi-Robot Task and Motion Planning](https://mlsanity.com/paper/2210.04333/) (2022)
- [Using Memory-Based Learning to Solve Tasks with State-Action Constraints](https://mlsanity.com/paper/2303.04327/) (2023)
- [TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policies](https://mlsanity.com/paper/2509.19712/) (2025)
- [Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework](https://mlsanity.com/paper/2606.27757/) (2026)
- [GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2303.01070/) (2023)
- [Learning from demonstrations: An intuitive VR environment for imitation learning of construction robots](https://mlsanity.com/paper/2305.14584/) (2023)
- [MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments](https://mlsanity.com/paper/2306.06543/) (2023)
- [Interpretable Responsibility Sharing as a Heuristic for Task and Motion Planning](https://mlsanity.com/paper/2409.05586/) (2024)
- [Prompting Robot Teams with Natural Language](https://mlsanity.com/paper/2509.24575/) (2025)
- [MO-SeGMan: Rearrangement Planning Framework for Multi Objective Sequential and Guided Manipulation in Constrained Environments](https://mlsanity.com/paper/2511.01476/) (2025)
- [ScheduleStream: Temporal Planning with Samplers for GPU-Accelerated Multi-Arm Task and Motion Planning & Scheduling](https://mlsanity.com/paper/2511.04758/) (2025)
- [EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly](https://mlsanity.com/paper/2605.18872/) (2026)

## Abstract (arXiv)

Robotic assembly planning enables architects to explicitly account for the assembly process during the design phase, and enables efficient building methods that profit from the robots' different capabilities. Previous work has addressed planning of robot assembly sequences and identifying the feasibility of architectural designs. This paper extends previous work by enabling planning with large, heterogeneous teams of robots. We present a planning system which enables parallelization of complex task and motion planning problems by iteratively solving smaller subproblems. Combining optimization methods to solve for manipulation constraints with a sampling-based bi-directional space-time path planner enables us to plan cooperative multi-robot manipulation with unknown arrival-times. Thus, our solver allows for completing subproblems and tasks with differing timescales and synchronizes them effectively. We demonstrate the approach on multiple case-studies to show the robustness over long planning horizons and scalability to many objects and agents of our algorithm. Finally, we also demonstrate the execution of the computed plans on two robot arms to showcase the feasibility in the real world.