用大模型实现工地机器人任务分配，效率提升超80%且更易调整。
Phase-Adaptive LLM Framework with Multi-Stage Validation for Construction Robot Task Allocation: A Systematic Benchmark Against Traditional Optimization Algorithms
- 基于大模型动态调整分配策略，分阶段验证并自动纠错。
- 任务完成率达77%，耗时减少86%，令牌消耗降低94.6%。
- 适合需要灵活调整、可解释性高的智能建造场景。
建筑自动化中的多机器人任务分配传统上依赖动态规划和强化学习等优化方法。本文提出基于LangGraph的任务分配代理（LTAA），一个融合阶段自适应策略、多阶段分层重试与动态提示的LLM驱动框架。该研究首次系统对比了基于大模型的任务分配与传统算法在建筑场景中的表现。通过复现SMART-LLM并采用自校正代理架构，解决了实现挑战。LTAA结合自然语言推理与结构化验证机制，在动态提示下实现显著计算增益：令牌使用减少94.6%，分配时间缩短86%。框架在不同阶段调整策略：早期侧重执行可行性，后期注重负载均衡。在TEACh人机协作数据集的Heavy Excels场景中，机器人具备强任务专长，LTAA达成77%的任务完成率，且负载平衡优于所有传统方法。结果表明，结合结构化验证的大模型推理可媲美经典优化算法，并具备可解释性、可适应性及无需重训练即可更新任务逻辑的优势。
原文摘要 · Abstract (English)
Multi-robot task allocation in construction automation has traditionally relied on optimization methods such as Dynamic Programming and Reinforcement Learning. This research introduces the LangGraph-based Task Allocation Agent (LTAA), an LLM-driven framework that integrates phase-adaptive allocation strategies, multi-stage validation with hierarchical retries, and dynamic prompting for efficient robot coordination. Although recent LLM approaches show potential for construction robotics, they largely lack rigorous validation and benchmarking against established algorithms. This paper presents the first systematic comparison of LLM-based task allocation with traditional methods in construction scenarios.The study validates LLM feasibility through SMART-LLM replication and addresses implementation challenges using a Self-Corrective Agent Architecture. LTAA leverages natural-language reasoning combined with structured validation mechanisms, achieving major computational gains reducing token usage by 94.6% and allocation time by 86% through dynamic prompting. The framework adjusts its strategy across phases: emphasizing execution feasibility early and workload balance in later allocations.The authors evaluate LTAA against Dynamic Programming, Q-learning, and Deep Q-Network (DQN) baselines using construction operations from the TEACh human-robot collaboration dataset. In the Heavy Excels setting, where robots have strong task specializations, LTAA achieves 77% task completion with superior workload balance, outperforming all traditional methods. These findings show that LLM-based reasoning with structured validation can match established optimization algorithms while offering additional advantages such as interpretability, adaptability, and the ability to update task logic without retraining.
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