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Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
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Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
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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.
Forward citations
Cited by 7 Pith papers
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Tri-Manual Visuomotor Imitation Learning of Robot Policies
Offline dependency-aware rescheduling of pairwise teleoperated demonstrations trains synchronous three-arm policies that are substantially faster with similar success.
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Tri-Manual Visuomotor Imitation Learning of Robot Policies
Retiming pairwise-teleoperated demonstrations with a dependency-aware scheduler lets one operator train synchronous three-arm robot policies that are faster and equally successful.
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VAMP-MR: Vector-Accelerated Motion Planning and Execution for Multi-Robot-Arms
A vectorized SIMD multi-robot collision checker accelerates multi-arm motion planning, shortcutting, and temporal-plan-graph execution by 10–150x over FCL baselines.
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ScheduleStream: Temporal Planning with Samplers for GPU-Accelerated Multi-Arm Task and Motion Planning & Scheduling
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.
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Diffusion-Guided Multi-Arm Motion Planning
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.
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Sampling-Based Multi-Modal Multi-Robot Multi-Goal Path Planning
Introduces probabilistically complete and asymptotically optimal sampling-based planners for multi-modal multi-robot multi-goal path planning by adapting standard methods to the composite space of all robots.
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Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teaming
RAPIDDS unifies task-level and motion-level adaptation in human-robot teaming by modeling individualized spatial and temporal behaviors across multiple cycles and jointly optimizing schedules and diffusion-based motions.
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