Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 8 of its 33 references that are in the map. First posted on arXiv 4 Mar 2022.
Builds on
8 of 33 references.
References · 8
- 2020Integrated Task and Motion Planningcited by 281
- 2018Searching with Consistent Prioritization for Multi-Agent Path Findingcited by 80
- 2021Long-Horizon Multi-Robot Rearrangement Planning for Construction Assemblycited by 24
- 2019Representation-Optimal Multi-Robot Motion Planning using Conflict-Based Searchcited by 16
- 2020Optimal Sequential Task Assignment and Path Finding for Multi-Agent Robotic Assembly Planningcited by 13
- 2020Synchronized Multi-Arm Rearrangement Guided by Mode Graphs with Capacity Constraintscited by 7
- 2020Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusioncited by 3
- 2021Robotic additive construction of bar structures: Unified sequence and motion planningcited by 3
Built on by
Citing papers per year: 2023: 2; 2024: 1; 2025: 2; 2026: 2.
7 papers.
- 2023Heterogeneous Embodied Multi-Agent Collaborationcited by 6
- 2025Diffusion-Guided Multi-Arm Motion Planningcited by 2
- 2024Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learningcited by 1
- 2023MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environmentscited by 0
- 2025ScheduleStream: Temporal Planning with Samplers for GPU-Accelerated Multi-Arm Task and Motion Planning & Schedulingcited by 0
- 2026Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teamingcited by 0
- 2026Tri-Manual Visuomotor Imitation Learning of Robot Policiescited by 0
- 2026Tri-Manual Visuomotor Imitation Learning of Robot Policies28 Jul 2026
- 2026Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teaming21 Apr 2026
- 2025ScheduleStream: Temporal Planning with Samplers for GPU-Accelerated Multi-Arm Task and Motion Planning & Scheduling6 Nov 2025
- 2025Diffusion-Guided Multi-Arm Motion Planning9 Sep 2025
- 2024Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning28 Sep 2024
- 2023Heterogeneous Embodied Multi-Agent Collaboration26 Jul 2023
- 2023MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments10 Jun 2023