Few-Shot Demonstration-Driven Task Coordination and Trajectory Execution for Multi-Robot Systems

2025-10-17

AI Summary

Multi-robot systems struggle with task coordination and trajectory execution under few-shot learning conditions. Method: This paper proposes DDACE—a decoupled framework that separately models temporal task sequencing via temporal graph networks and spatial trajectory generation via Gaussian processes—enabling task-agnostic temporal modeling and spatial generalization. DDACE requires only a small number of demonstrations to achieve multi-sequence manipulation, complex trajectory synthesis, and heterogeneous robot coordination in dynamic environments. Contribution/Results: Experiments demonstrate that DDACE significantly outperforms baseline methods in generalization capability, trajectory accuracy, and modular design. It substantially reduces data dependency while enhancing system scalability and deployment efficiency.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Coordination and Collaboration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In this paper, we propose a novel few-shot learning framework for multi-robot systems that integrate both spatial and temporal elements: Few-Shot Demonstration-Driven Task Coordination and Trajectory Execution (DDACE). Our approach leverages temporal graph networks for learning task-agnostic temporal sequencing and Gaussian Processes for spatial trajectory modeling, ensuring modularity and generalization across various tasks. By decoupling temporal and spatial aspects, DDACE requires only a small number of demonstrations, significantly reducing data requirements compared to traditional learning from demonstration approaches. To validate our proposed framework, we conducted extensive experiments in task environments designed to assess various aspects of multi-robot coordination-such as multi-sequence execution, multi-action dynamics, complex trajectory generation, and heterogeneous configurations. The experimental results demonstrate that our approach successfully achieves task execution under few-shot learning conditions and generalizes effectively across dynamic and diverse settings. This work underscores the potential of modular architectures in enhancing the practicality and scalability of multi-robot systems in real-world applications. Additional materials are available at https://sites.google.com/view/ddace.

Problem

• Develops few-shot learning for multi-robot task coordination and trajectory execution
• Reduces demonstration data needs by decoupling temporal and spatial elements
• Enables generalization across dynamic heterogeneous multi-robot configurations

Innovation

• Few-shot learning framework for multi-robot systems
• Temporal graph networks for task sequencing
• Gaussian Processes for spatial trajectory modeling