让机器人在动态拆解中自适应学习，提升成功率至100%
Embodied Intelligence in Disassembly: Multimodal Perception Cross-validation and Continual Learning in Neuro-Symbolic TAMP
- 用多模态感知交叉验证与双向推理流实现持续学习
- 动态拆解任务成功率从81.68%提升至100%
- 适合工业级机器人自主拆解场景研究者
随着新能源汽车产业的快速发展，动力电池的高效拆解与回收已成为循环经济的关键挑战。在当前非结构化拆解场景中，环境的动态性严重制约了机器人感知的鲁棒性，成为工业应用中实现自主拆解的主要障碍。本文提出一种基于神经符号任务与运动规划（Neuro-Symbolic 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能力。该方法将多模态感知交叉验证机制融入双向推理流程：前向工作流动态优化动作策略，后向学习流则自主从历史任务执行中收集有效数据，推动系统持续学习，实现自我优化。实验结果表明，所提框架使动态拆解场景下的任务成功率从81.68%提升至100%，平均感知误判次数由3.389次降至1.128次。本研究为复杂工业环境中具身智能的鲁棒性与适应性提升提供了新范式。
原文摘要 · Abstract (English)
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.
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