Pith. sign in

REVIEW 6 cited by

Cooperative Task and Motion Planning for Multi-Arm Assembly Systems

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2203.02475 v1 pith:BR2ON7EP submitted 2022-03-04 cs.RO cs.AI

classification cs.ROcs.AI
keywords planningrobotsabstractassemblyplanplanssystemsapproach
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Tri-Manual Visuomotor Imitation Learning of Robot Policies

    cs.RO 2026-07 conditional novelty 7.0 of 10

    Retiming pairwise-teleoperated demonstrations with a dependency-aware scheduler lets one operator train synchronous three-arm robot policies that are faster and equally successful.

  2. Coordinated Multi-Robot Disassembly for Makespan Optimization of Large-Scale Assemblies

    cs.RO 2026-08 conditional novelty 6.0 of 10

    CoMuDi plans coordinated, collision-free multi-robot disassembly trajectories using prioritized task queues, temporal constraint propagation, and ST-RRT*, reducing makespan on simulated assemblies with up to 49 parts.

  3. VAMP-MR: Vector-Accelerated Motion Planning and Execution for Multi-Robot-Arms

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A vectorized SIMD multi-robot collision checker accelerates multi-arm motion planning, shortcutting, and temporal-plan-graph execution by 10–150x over FCL baselines.

  4. ScheduleStream: Temporal Planning with Samplers for GPU-Accelerated Multi-Arm Task and Motion Planning & Scheduling

    cs.RO 2025-11 conditional novelty 6.0 of 10

    ScheduleStream extends sampling-based task and motion planning with durative actions and temporal scheduling so a bimanual robot can plan and execute parallel arm motions, roughly halving makespan versus sequential planning.

  5. Diffusion-Guided Multi-Arm Motion Planning

    cs.RO 2025-09 conditional novelty 6.0 of 10

    A MAPF-inspired search guided by single-arm and dual-arm diffusion models plans collision-free motions for many arms without higher-order training data.

  6. Simulation-based planning of Motion Sequences for Automated Procedure Optimization in Multi-Robot Assembly Cells

    cs.RO 2025-07 conditional novelty 6.0 of 10

    A scheduling and motion-planning framework that decomposes assembly steps into fixed core operations and flexible traverse motions reduced simulated assembly duration by 10-20% versus a decentralized baseline.

Pith tools