arXiv:2606.19920v1 Announce Type: cross Abstract: Distributed optimization is a highly scalable and structurally transparent technique to solve multi-agent robotics problems; however, such methods often suffer from the need for highly-specialized, problem-specific hyperparameter tunings. In this work, we propose Deep Coordinator, a deep-unfolding framework that learns to dynamically adjust the hyperparameters of ADMM-DDP, a popular distributed solver for robotics tasks, at solve-time in response to optimizer performance. Our architecture consists of unrolling a fixed number of ADMM-DDP iterati
The increasing complexity of multi-agent robotic systems and the limitations of traditional distributed optimization methods necessitate more adaptive and autonomous solutions.
This development addresses a critical bottleneck in the scalability and deployment of multi-agent robotic systems by making their coordination more efficient and less dependent on manual tuning.
The ability of distributed optimization algorithms to dynamically self-adjust hyperparameters will improve their performance and applicability, accelerating the development of complex robotic systems.
- · Robotics research institutions
- · Companies developing autonomous systems
- · AI software developers
- · Logistics and manufacturing sectors
- · Developers reliant on manual hyperparameter tuning
- · Systems with fixed-parameter coordination models
More robust and efficient multi-agent robotic deployments in various applications.
Accelerated integration of autonomous robot fleets into industrial and defense operations.
Enhanced operational capabilities for complex autonomous swarms and integrated AI agent systems across critical infrastructure.
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Read at arXiv cs.LG