Deep-Unfolded Coordination

2026-06-19 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

Deep Coordinator is a novel deep-unfolding framework designed to accelerate distributed optimization in multi-agent robotics by dynamically adjusting ADMM-DDP hyperparameters at solve-time. Developed by researchers at Georgia Institute of Technology, this architecture unrolls a fixed number of ADMM-DDP iterations into a neural network, learning feedback policies to map optimizer states to next hyperparameters. It is the first deep-unfolding framework to adapt non-convex optimizer penalty parameters at solve-time, employing an unsupervised learning scheme to avoid degenerate solutions. Benchmarked on simulations with fleets of cars and quadrotors, Deep Coordinator produces comparable quality trajectories 6.18-9.44x faster than conventional solvers. Crucially, it maintains performance benefits when deployed to systems up to 8x larger than those used for training.

Key takeaway

For Robotics Engineers developing multi-agent control systems, Deep Coordinator offers a significant speedup for complex coordination tasks. You can achieve 6.18-9.44x faster trajectory generation with comparable quality by integrating this deep-unfolding framework. Its ability to generalize to systems 8x larger than trained on means you can develop robust solutions without extensive retraining, making it highly practical for scalable deployments in areas like drone swarms or autonomous vehicle fleets.

Key insights

Deep Coordinator accelerates multi-agent robotics optimization by dynamically learning ADMM-DDP hyperparameters via deep-unfolding and unsupervised training.

Principles

• Deep-unfolding excels in multi-agent robotics due to numerous tunable parameters.
• Model-predictive control (MPC) contexts benefit from specialized hyperparameter policies.
• Unsupervised loss is crucial for training deep-unfolded non-convex optimizers.

Method

Deep Coordinator unrolls K ADMM-DDP iterations into a neural network with learnable feedback policies. It uses an unsupervised loss function penalizing cost and constraint violation, and the Implicit Function Theorem (IFT) for efficient gradient computation.

In practice

• Apply shared per-agent, per-timestep policies for scalability across agent numbers.
• Utilize barrier-augmented differentiation for smooth gradients in constrained problems.
• Deploy models trained on smaller systems to significantly larger multi-agent fleets.

Topics

• Deep Unfolding
• Multi-Agent Robotics
• Distributed Optimization
• ADMM-DDP
• Hyperparameter Adaptation
• Unsupervised Learning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.