arXiv AI
AssemblyGrid v1: A Benchmark for Multi-Robot Production with Temporary Coalitions, Local Information, and Geometric Constraints
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Learning Multi-Agent Task Assignment and Navigation in the Factory: from Simulation to Real Robots
arXiv:2609.14567v1 Announce Type: cross Abstract: Reinforcement learning (RL) has shown considerable promise for robotic decision-making, yet deploying multi-agent RL (MARL) on physical multi-robot s...
arXiv Machine Learning
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Optimization without Future Compromises? Decentralized Coordination via Collective and Reinforcement Learning
The paper introduces Hierarchical Reinforcement and Collective Learning (HRCL), a framework that combines multi‑agent reinforcement learning (MARL) with decentralized coordination. HRCL uses MARL at a high level to generate strategic guidance that limits the decision space for low‑level agents, enabling efficient short‑term coordination while considering long‑term effects. Experiments on synthetic, energy‑management, and drone‑swarm scenarios demonstrate faster convergence and significant reductions in system‑wide and individual costs compared to standalone MARL.
arXiv AI
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CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy
arXiv:2606. 12352v1 Announce Type: cross Abstract: Multi-robot collaboration allows robots to efficiently take on a wide range of tasks, from moving a couch through a doorway to assembling structures on a construction site.
arXiv AI
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REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation
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arXiv AI
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LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints
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arXiv AI
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cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation
cuArms is a GPU-accelerated conflict-based planner designed for multi-arm manipulation tasks. It decomposes the search space using reusable multi-modal roadmaps for each robot and resolves conflicts and deadlocks via a lazy search, while a high-level scheduler proposes candidate skeletons for joint optimization of task assignment, schedules, motions, and manipulation skills. Experimental results demonstrate real-time initial solutions and anytime improvements in motion quality and scheduling, outperforming previous methods that required extensive domain-specific training.