robotics
Factory Robots Formed Temporary Coalitions With Local Information
AssemblyGrid separates task success from learning reward across flow, coalition and concurrency workloads.
Summary
AssemblyGrid separates task success from learning reward across flow, coalition and concurrency workloads.
Flexible production can require robots to route material, share resources, form temporary teams and work concurrently while each sees only local information. AssemblyGrid v1 combines those constraints with geometry-dependent feasibility in one repeatable benchmark. It defines three workload families at three scenario levels, with executable conformance checks and success metrics independent of any learning reward. Centralized references, structured decentralized controllers and IPPO, MAPPO and QMIX experiments all produced productive behavior. The benchmark is a controlled task-level environment, not evidence of autonomous deployment on a factory floor.
Why it matters
AssemblyGrid separates task success from learning reward across flow, coalition and concurrency workloads.
Limits and context
- Flexible production can require robots to route material, share resources, form temporary teams and work concurrently while each sees only local information.
- The benchmark is a controlled task-level environment, not evidence of autonomous deployment on a factory floor.
Key claims
AssemblyGrid separates task success from learning reward across flow, coalition and concurrency workloads.
Qualification: Flexible production can require robots to route material, share resources, form temporary teams and work concurrently while each sees only local information.
Evidence: source-2026-09-16-013
Sources
- arXiv preprint 2609.16075arXiv · primary research
Corrections
No corrections have been recorded for this story.