Learning to Coordinate: Distributed Meta-Trajectory Optimization Via Differentiable ADMM-DDP
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| Format: | Preprint |
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2025
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| _version_ | 1866916935431618560 |
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| author | Wang, Bingheng Gao, Yichao Sun, Tianchen Zhao, Lin |
| author_facet | Wang, Bingheng Gao, Yichao Sun, Tianchen Zhao, Lin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_01630 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Coordinate: Distributed Meta-Trajectory Optimization Via Differentiable ADMM-DDP Wang, Bingheng Gao, Yichao Sun, Tianchen Zhao, Lin Machine Learning Multiagent Systems Robotics Systems and Control 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. |
| title | Learning to Coordinate: Distributed Meta-Trajectory Optimization Via Differentiable ADMM-DDP |
| topic | Machine Learning Multiagent Systems Robotics Systems and Control |
| url | https://arxiv.org/abs/2509.01630 |