拆卸中的具身智能：神经符号TAMP中的多模态感知交叉验证与持续学习
Embodied Intelligence in Disassembly: Multimodal Perception Cross-validation and Continual Learning in Neuro-Symbolic TAMP
AI总结：
本文提出基于神经符号TAMP的持续学习框架，集成多模态感知交叉验证与双向推理流，解决动力电池非结构化拆卸中的动态环境感知问题，将任务成功率提升至100%并显著降低感知误判。
摘要：
随着新能源汽车产业的快速发展，动力电池的高效拆卸与回收已成为循环经济的关键挑战。在当前非结构化拆卸场景中，环境的动态特性严重限制了机器人感知的鲁棒性，对工业应用中的自主拆卸构成了重大障碍。本文提出了一种基于神经符号任务与运动规划（TAMP）的持续学习框架，以增强具身智能系统在动态环境中的适应性。该方法将多模态感知交叉验证机制集成到双向推理流中：前向工作流动态细化并优化动作策略，而后向学习流则从历史任务执行中自主收集有效数据以促进系统持续学习，实现自我优化。实验结果表明，所提出的框架将动态拆卸场景中的任务成功率从81.68%提升至100%，同时将平均感知误判次数从3.389减少至1.128。本研究为增强复杂工业环境中具身智能的鲁棒性和适应性提供了新范式。
英文摘要：
With the rapid development of the new energy vehicle industry, the efficient disassembly and recycling of power batteries have become a critical challenge for the circular economy. In current unstructured disassembly scenarios, the dynamic nature of the environment severely limits the robustness of robotic perception, posing a significant barrier to autonomous disassembly in industrial applications. This paper proposes a continual learning framework based on Neuro-Symbolic task and motion planning (TAMP) to enhance the adaptability of embodied intelligence systems in dynamic environments. Our approach integrates a multimodal perception cross-validation mechanism into a bidirectional reasoning flow: the forward working flow dynamically refines and optimizes action strategies, while the backward learning flow autonomously collects effective data from historical task executions to facilitate continual system learning, enabling self-optimization. Experimental results show that the proposed framework improves the task success rate in dynamic disassembly scenarios from 81.68% to 100%, while reducing the average number of perception misjudgments from 3.389 to 1.128. This research provides a new paradigm for enhancing the robustness and adaptability of embodied intelligence in complex industrial environments.
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。
