用大模型理解人话，动态规划机器人交接任务。
LLM-Grounded Dynamic Task Planning with Hierarchical Temporal Logic for Human-Aware Multi-Robot Handover
- 将人话转为分层时序逻辑规范，实现动态任务规划
- 在真实机器人上成功率达94%，重规划开销降低60%
- 适合非专家使用，支持环境变化下的实时调整
大型语言模型（LLMs）使非专家能够指定开放世界中的多机器人任务，但生成的计划常存在运动学不可行且在长时域下效率低下。形式化方法如线性时序逻辑（LTL）虽能保证正确性和最优性，却通常为离线计算且扩展性差。为此，我们提出一种神经符号框架，将人类指令转化为分层LTLf规范（即有限轨迹上的LTL），并求解相应的联合任务分配与规划（STAP）问题。与静态方法不同，本系统通过滚动时域规划（RHP）循环结合实时感知，处理用户移动或指令更新等随机环境变化，在分层状态空间中动态优化计划。仿真及真实机器人实验表明，该方法在成功率和交互流畅性上显著优于基线，同时将重规划开销减少60%。
原文摘要 · Abstract (English)
Large Language Models (LLMs) enable non-experts to specify open-world multi-robot tasks, but the generated plans are often kinematically infeasible and inefficient in long-horizon settings. Formal methods such as Linear Temporal Logic (LTL) offer correctness and optimality guarantees, yet they are typically offline and scale poorly. To bridge this gap, we propose a neuro-symbolic framework that grounds human instructions into hierarchical LTLf specifications (i.e., LTL on finite traces) and solves the resulting Simultaneous Task Allocation and Planning (STAP) problem. Unlike static approaches, our system handles stochastic environmental changes-such as user motion or updated instructions-through a receding horizon planning (RHP) loop with real-time perception, dynamically refining plans over a hierarchical state space. Experiments in simulation and on real robots demonstrate that our approach significantly outperforms baseline methods in success rate and interaction fluency while reducing replanning overhead.
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