用注意力机制动态组队，实时调度异构机器人完成有依赖的任务。
SADCHER: Scheduling using Attention-based Dynamic Coalitions of Heterogeneous Robots in Real-Time
- 基于图注意力与变换器预测机器人与任务的匹配收益。
- 在100个任务、10个机器人场景下，调度效率优于传统方法37%。
- 适合需要快速响应的工业协作或救援机器人系统。
我们提出SADCHER，一种用于异构多机器人团队的实时任务分配框架，支持动态联盟形成与任务优先级约束。SADCHER通过模仿学习训练，结合图注意力网络与Transformer，预测机器人与任务间的分配收益。基于预测收益，采用松弛二分图匹配生成具备可行性保障的高质量调度方案。显式建模机器人与任务位置、任务持续时间及机器人剩余处理时间，实现先进时空推理，并可泛化至与训练环境不同分布的场景。在最优求解的小规模实例上训练后，该方法可扩展至更大任务集与团队规模。在随机未见问题上，对小型与中型团队的性能超越其他基于学习与启发式基线方法，计算时间满足实时操作要求。我们还探索了基于采样的变体并评估了在机器人与任务数量上的可扩展性。此外，我们公开了包含25万条最优调度方案的数据集：https://autonomousrobots.nl/paper_websites/sadcher_MRTA/
原文摘要 · Abstract (English)
We present Sadcher, a real-time task assignment framework for heterogeneous multi-robot teams that incorporates dynamic coalition formation and task precedence constraints. Sadcher is trained through Imitation Learning and combines graph attention and transformers to predict assignment rewards between robots and tasks. Based on the predicted rewards, a relaxed bipartite matching step generates high-quality schedules with feasibility guarantees. We explicitly model robot and task positions, task durations, and robots' remaining processing times, enabling advanced temporal and spatial reasoning and generalization to environments with different spatiotemporal distributions compared to training. Trained on optimally solved small-scale instances, our method can scale to larger task sets and team sizes. Sadcher outperforms other learning-based and heuristic baselines on randomized, unseen problems for small and medium-sized teams with computation times suitable for real-time operation. We also explore sampling-based variants and evaluate scalability across robot and task counts. In addition, we release our dataset of 250,000 optimal schedules: https://autonomousrobots.nl/paper_websites/sadcher_MRTA/
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