Learning to Coordinate: Distributed Meta-Trajectory Optimization Via Differentiable ADMM-DDP

• URL: http://arxiv.org/abs/2509.01630v2
• Date: Fri, 05 Sep 2025 15:36:28 GMT
• Title: Learning to Coordinate: Distributed Meta-Trajectory Optimization Via Differentiable ADMM-DDP
• Authors: Bingheng Wang, Yichao Gao, Tianchen Sun, Lin Zhao,
• Abstract summary: We propose Learning to Coordinate (L2C) to adapt across diverse tasks and agent configurations.<n>L2C differentiates end-to-end through the ADMM-DDP pipeline in a distributed manner.<n>It achieves up to $88%$ faster gradient computation than state-of-the-art methods.
• Score: 4.880846795915428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributed trajectory optimization via ADMM-DDP is a powerful approach for coordinating multi-agent systems, but it requires extensive tuning of tightly coupled hyperparameters that jointly govern local task performance and global coordination. In this paper, we propose Learning to Coordinate (L2C), a general framework that meta-learns these hyperparameters, modeled by lightweight agent-wise neural networks, to adapt across diverse tasks and agent configurations. L2C differentiates end-to-end through the ADMM-DDP pipeline in a distributed manner. It also enables efficient meta-gradient computation by reusing DDP components such as Riccati recursions and feedback gains. These gradients correspond to the optimal solutions of distributed matrix-valued LQR problems, coordinated across agents via an auxiliary ADMM framework that becomes convex under mild assumptions. Training is further accelerated by truncating iterations and meta-learning ADMM penalty parameters optimized for rapid residual reduction, with provable Lipschitz-bounded gradient errors. On a challenging cooperative aerial transport task, L2C generates dynamically feasible trajectories in high-fidelity simulation using IsaacSIM, reconfigures quadrotor formations for safe 6-DoF load manipulation in tight spaces, and adapts robustly to varying team sizes and task conditions, while achieving up to $88\%$ faster gradient computation than state-of-the-art methods.

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