# Cooperative Task and Motion Planning for Multi-Arm Assembly Systems

arXiv: https://arxiv.org/abs/2203.02475 · HTML: https://mlsanity.com/paper/2203.02475/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 4 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [Searching with Consistent Prioritization for Multi-Agent Path Finding](https://mlsanity.com/paper/1812.06356/) (2018)
- [Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly](https://mlsanity.com/paper/2106.02489/) (2021)
- [Representation-Optimal Multi-Robot Motion Planning using Conflict-Based Search](https://mlsanity.com/paper/1909.13352/) (2019)
- [Optimal Sequential Task Assignment and Path Finding for Multi-Agent Robotic Assembly Planning](https://mlsanity.com/paper/2006.08845/) (2020)
- [Synchronized Multi-Arm Rearrangement Guided by Mode Graphs with Capacity Constraints](https://mlsanity.com/paper/2005.09127/) (2020)
- [Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion](https://mlsanity.com/paper/2002.02360/) (2020)
- [Robotic additive construction of bar structures: Unified sequence and motion planning](https://mlsanity.com/paper/2105.11438/) (2021)

## Built on by (most cited first, 7 of 7)

- [Heterogeneous Embodied Multi-Agent Collaboration](https://mlsanity.com/paper/2307.13957/) (2023)
- [Diffusion-Guided Multi-Arm Motion Planning](https://mlsanity.com/paper/2509.08160/) (2025)
- [Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning](https://mlsanity.com/paper/2409.19226/) (2024)
- [MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments](https://mlsanity.com/paper/2306.06543/) (2023)
- [ScheduleStream: Temporal Planning with Samplers for GPU-Accelerated Multi-Arm Task and Motion Planning & Scheduling](https://mlsanity.com/paper/2511.04758/) (2025)
- [Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teaming](https://mlsanity.com/paper/2604.19670/) (2026)
- [Tri-Manual Visuomotor Imitation Learning of Robot Policies](https://mlsanity.com/paper/2607.25731/) (2026)

## Abstract (arXiv)

Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs. However, effectively planning for these systems in a manner that ensures each robot is simultaneously productive, and not idle, is challenging due to (1) the close proximity that the robots must operate in to manipulate the structure and (2) the inherent structural partial orderings on when each part can be installed. In this paper, we present a task and motion planning framework that jointly plans safe, low-makespan plans for a team of robots to assemble complex spatial structures. Our framework takes a hierarchical approach that, at the high level, uses Mixed-integer Linear Programs to compute an abstract plan comprised of an allocation of robots to tasks subject to precedence constraints and, at the low level, builds on a state-of-the-art algorithm for Multi-Agent Path Finding to plan collision-free robot motions that realize this abstract plan. Critical to our approach is the inclusion of certain collision constraints and movement durations during high-level planning, which better informs the search for abstract plans that are likely to be both feasible and low-makespan while keeping the search tractable. We demonstrate our planning system on several challenging assembly domains with several (sometimes heterogeneous) robots with grippers or suction plates for assembling structures with up to 23 objects involving Lego bricks, bars, plates, or irregularly shaped blocks.