基于分层时序逻辑的LLM引导动态任务规划用于人感知多机器人协作
LLM-Grounded Dynamic Task Planning with Hierarchical Temporal Logic for Human-Aware Multi-Robot Handover
AI总结：
本文提出基于分层时序逻辑的神经符号框架，实现动态任务规划以提升多机器人协作的效率和鲁棒性。
摘要：
尽管大型语言模型（LLM）使非专业人士能够指定开放世界多机器人任务，但生成的计划往往缺乏运动学可行性且效率低下，特别是在长时间范围场景中。形式化方法如线性时序逻辑（LTL）提供了正确性和最优保证，但通常局限于静态、离线环境，并在计算可扩展性上遇到困难。为此，我们提出了一种神经符号框架，将LLM推理接地到分层LTL规范中，并解决相应的同时任务分配与规划（STAP）问题。与静态方法不同，我们的系统通过递推时间规划（RHP）循环实时感知，解决随机环境变化，如移动用户或更新的指令，通过分层状态空间动态细化计划。大量现实世界实验表明，我们的方法在成功率和交互流畅度方面显著优于基线方法，同时最小化规划延迟。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
