Deep-Unfolded Coordination
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 iterations into a neural network with learnable functions between layers mapping the optimizer state to the next hyperparameters. To the best of our knowl...
| Authors | Hunter Kuperman, Minchan Jung, Rahul V. Ghosh et al. |
|---|---|
| Primary category | cs.RO |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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