面向选择性机器人拆解的视觉-语言-动作模型：以从台式机中提取关键部件的案例研究
Vision-Language-Action Models for Selective Robotic Disassembly: A Case Study on Critical Component Extraction from Desktops
AI总结：
本文研究了视觉-语言-动作模型在机器人选择性拆解中的应用，通过定制数据集微调两种VLA方法，发现其在复杂拆解任务中存在局限，但结合规则控制器的混合策略能有效完成拆解任务。
摘要：
自动化从终端生命周期（EoL）台式机中拆解关键部件，如高价值的RAM模块和CPU，以及敏感部件如硬盘驱动器，仍然具有挑战性，这归因于这些产品的固有变异性与不确定性。此外，其拆解需要顺序、精确和灵巧的操作，进一步增加了自动化的复杂性。当前的机器人拆解过程通常分为几个阶段：感知、序列规划、任务规划、运动规划和操作。每个阶段都需要显式建模，这限制了对不熟悉场景的泛化能力。最近视觉-语言-动作（VLA）模型的发展提供了一种端到端的方法来处理通用机器人操作任务。尽管VLAs在简单任务上表现出色，但将此类模型应用于复杂拆解的可行性仍鲜有探索。在本文中，我们收集了一个定制的机器人RAM和CPU拆解数据集，并使用它来微调两个已建立的VLA方法，即OpenVLA和OpenVLA-OFT，作为案例研究。我们将整个拆解任务分成几个小步骤，我们的初步实验结果表明，微调的VLA模型能够忠实完成多个早期步骤，但对某些关键子任务却力不从心，导致任务失败。然而，我们观察到，一种简单的混合策略，将VLA与基于规则的控制器结合，可以成功完成整个拆解操作。这些发现突显了当前VLA模型在处理机器人EoL产品拆解所需的灵巧性和精度方面的局限性。通过详细分析观察到的结果，本研究提供了见解，可能为未来研究提供指导，以解决当前挑战并推进端到端机器人自动化拆解。
英文摘要：
Automating disassembly of critical components from end-of-life (EoL) desktops, such as high-value items like RAM modules and CPUs, as well as sensitive parts like hard disk drives, remains challenging due to the inherent variability and uncertainty of these products. Moreover, their disassembly requires sequential, precise, and dexterous operations, further increasing the complexity of automation. Current robotic disassembly processes are typically divided into several stages: perception, sequence planning, task planning, motion planning, and manipulation. Each stage requires explicit modeling, which limits generalization to unfamiliar scenarios. Recent development of vision-language-action (VLA) models has presented an end-to-end approach for general robotic manipulation tasks. Although VLAs have demonstrated promising performance on simple tasks, the feasibility of applying such models to complex disassembly remains largely unexplored. In this paper, we collected a customized dataset for robotic RAM and CPU disassembly and used it to fine-tune two well-established VLA approaches, OpenVLA and OpenVLA-OFT, as a case study. We divided the whole disassembly task into several small steps, and our preliminary experimental results indicate that the fine-tuned VLA models can faithfully complete multiple early steps but struggle with certain critical subtasks, leading to task failure. However, we observed that a simple hybrid strategy that combines VLA with a rule-based controller can successfully perform the entire disassembly operation. These findings highlight the current limitations of VLA models in handling the dexterity and precision required for robotic EoL product disassembly. By offering a detailed analysis of the observed results, this study provides insights that may inform future research to address current challenges and advance end-to-end robotic automated disassembly.
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