DiffCoord: 分散マルチエージェント軌道最適化のための微分可能な協調
DiffCoord: Differentiable Coordination for Distributed Multi-Agent Trajectory Optimization
ADMMとDDPを組み合わせた分散軌道最適化において、協調品質とタスク性能を決めるパラメータをニューラルネットでメタ学習し、エンドツーエンド微分で効率的に最適化する手法を提案。協調航空輸送で有効性を実証。
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著者: Bingheng Wang, Yichao Gao, Tianchen Sun, Shanker Ajay, Lin Zhao
分類: cs.LG, cs.MA, cs.RO, cs.SY, eess.SY
原文アブストラクト
Integrating the Alternating Direction Method of Multipliers (ADMM) with Differential Dynamic Programming (DDP) provides a scalable framework for distributed multi-agent trajectory optimization. In practice, ADMM is typically truncated for computational efficiency, tightly coupling parameters that would otherwise separately govern coordination quality and task performance. In this paper, we propose Differentiable Coordination (DiffCoord), a unified framework that jointly meta-learns these coupled parameters for the truncated ADMM-DDP pipeline. These parameters are generated by agent-wise neural networks for task adaptation, and the same networks are shared among isomorphic agents to enable scalability to varying agent counts. We achieve efficient meta-learning by differentiating the ADMM-DDP pipeline end-to-end. Notably, this yields an auxiliary ADMM-LQR distributed gradient solver that computes and coordinates meta-gradients with respect to these parameters. This solver inherits the computational structure of the pipeline, enabling reuse of key computation results and efficient parallelization over agents and along trajectory horizons. We validate DiffCoord through numerical and physical experiments on a cooperative aerial transport system, where it reconfigures quadrotor formations for safe 6-DoF load manipulation in tight spaces. It adapts robustly to varying team sizes and load dynamics, while reducing per-agent gradient computation time by up to 70% compared with state-of-the-art trajectory-gradient methods.
関連論文
- GPUで高速化するマルチエージェント軌道最適化の凸近似マルチエージェント軌道最適化
