多机器人系统的少样本示范驱动任务协调与轨迹执行
Few-Shot Demonstration-Driven Task Coordination and Trajectory Execution for Multi-Robot Systems
AI总结：
研究如何让多机器人系统从少量示范中学习协调行为，核心方法是将时间协调与空间轨迹合成解耦，主要贡献是提出DDACE框架，提高少样本数据效率与轨迹一致性。
摘要：
从少量示范中学习多机器人系统的协调行为具有挑战性，因为时间任务依赖和空间轨迹生成紧密耦合。我们提出了DDACE，一种结构化少样本学习框架，通过明确解耦时间协调和空间轨迹合成来引入结构归纳偏差。实验表明DDACE在少量示范下能产生稳定协调执行并提高轨迹一致性。
英文摘要：
Learning coordinated behaviors for multi-robot systems from only a few demonstrations is difficult because temporal task dependencies and spatial trajectory generation are tightly coupled, which increases the hypothesis space and often yields unstable generalization in data-scarce regimes. We present DDACE, a structured few-shot learning framework that introduces a structural inductive bias by explicitly decoupling temporal coordination from spatial trajectory synthesis. Demonstrations are first processed via spectral clustering to extract coordination structure and form interaction graphs. A Temporal Graph Network predicts action dependencies and sequences, while Gaussian Process models generate progress-parameterized geometric trajectories that adapt to new start/goal configurations. This factorized design reduces hypothesis coupling and improves data efficiency for few-shot multi-robot coordination. Extensive simulation studies and real-robot experiments show that DDACE produces stable coordinated executions from a small number of demonstrations and improves trajectory consistency compared to end-to-end imitation baselines under limited data. Additional materials are available at https://sites.google.com/view/ddace.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
