面向人机协同工业机器人的智能神经符号规划与调试：基于数字孪生
Agentic Neuro-Symbolic Planning and Commissioning for Human-in-the-Loop Industrial Robotics with Digital Twins
AI总结：
提出一种结合LLM语言理解与确定性验证执行的神经符号框架，采用SDI架构和两级恢复机制，在数字孪生中验证后执行，显著提升任务成功率。
摘要：
灵活的机器人自动化需要系统能够解释操作员意图、验证物理可行性，并在规划和执行阶段从执行失败中恢复。本文提出了一种面向人机协同工业机器人的智能神经符号框架，其中LLM用于需要语言理解或上下文推理的任务，而所有验证、排序和执行保持确定性。该框架将软件工程中的规划器-生成器-评估器（PGE）模式改编为面向工业机器人的指定器-设计器-检查器（SDI）架构，并结合基于LangGraph的动态路由进行故障恢复。两级恢复机制通过上下文感知编排处理结构级重新规划，并通过确定性恢复技能处理执行级几何故障。Unity3D数字孪生支持在物理执行前进行人工检查、修改和重新验证。在多个难度级别的自然语言命令上对十个基线进行评估，所提方法实现了最高的任务成功率。消融结果证实，结构化命令扩展、符号验证、选择性LLM路由和恢复技能各自都是必要的。
英文摘要：
Flexible robotic automation requires systems that interpret operator intent, verify physical feasibility, and recover from execution failures across both the planning and execution stages. This paper proposes an agentic neuro-symbolic framework for human-in-the-loop industrial robotics, in which LLMs are used for tasks that require language understanding or contextual reasoning, while all verification, sequencing, and execution remain deterministic. The framework adapts the Planner-Generator-Evaluator (PGE) harness pattern from software engineering into a Specifier-Designer-Inspector (SDI) architecture for industrial robotics, combined with LangGraph-based dynamic routing for failure recovery. A two-tier recovery mechanism addresses structure-level replanning through context-aware orchestration and execution-level geometric failures through deterministic recovery skills. A Unity3D digital twin supports human inspection, modification, and re-verification prior to physical execution. Evaluated on natural-language commands across multiple difficulty levels against ten baselines, the proposed method achieves the highest task success. Ablation results confirm that structured command expansion, symbolic verification, selective LLM routing, and recovery skills are each individually necessary.
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。
