让机器人听懂人话并自动纠错，提升工业场景人机协作效率
Agentic Neuro-Symbolic Planning and Commissioning for Human-in-the-Loop Industrial Robotics with Digital Twins
- 用语言模型理解指令，符号验证保证执行安全
- 双层容错机制：结构重规划+几何级修复，成功率最高
- 数字孪生支持人工预审修改，适合复杂制造场景
柔性机器人自动化需系统能理解操作员意图、验证物理可行性，并在规划与执行阶段应对失败。本文提出一种面向人机协同工业机器人的智能体神经符号框架，利用大语言模型处理语言理解或上下文推理任务，而所有验证、排序与执行保持确定性。将软件工程中的规划-生成-评估（PGE）模式改造为指定-设计-检查（SDI）架构，并结合基于LangGraph的动态路由实现故障恢复。采用两级恢复机制：通过上下文感知编排处理结构级重规划，通过确定性恢复技能应对执行级几何错误。使用Unity3D数字孪生支持人工检查、修改与重新验证，再进行物理执行。在多难度自然语言指令上对比十种基线方法，本方法任务成功率最高。消融实验表明，结构化指令扩展、符号验证、选择性大语言模型路由和恢复技能均不可或缺。
原文摘要 · Abstract (English)
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.
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