用少量示范实现多机器人稳定协同，解耦任务时序与轨迹生成。
Few-Shot Demonstration-Driven Task Coordination and Trajectory Execution for Multi-Robot Systems
- 分离任务时序与轨迹生成，降低学习复杂度
- 仅需少量示范即可稳定生成协调动作序列
- 适合数据稀缺场景下的多机器人系统部署
从少量示范中学习多机器人系统的协同行为极具挑战，因时序任务依赖与空间轨迹生成紧密耦合，导致假设空间过大，在数据稀少情况下常出现不稳定泛化。本文提出DDACE框架，通过显式解耦时序协调与空间轨迹合成，引入结构归纳偏置。首先利用谱聚类处理示范数据，提取协调结构并构建交互图；再通过时序图网络预测动作依赖与顺序，结合高斯过程模型生成可适应新起点/目标配置的进度参数化几何轨迹。该分解设计减少假设耦合，提升少样本场景下的数据效率。大量仿真与真实机器人实验表明，DDACE能从少量示范中生成稳定协同执行结果，相较于端到端模仿学习基线，在有限数据下显著提升轨迹一致性。
原文摘要 · Abstract (English)
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.
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