一种用于快慢系统操作的自校正视觉-语言-动作模型
A Self-Correcting Vision-Language-Action Model for Fast and Slow System Manipulation
AI总结：
针对视觉-语言-动作模型（VLA）在复杂新任务中易失败的问题，提出融合快慢系统的自校正SC-VLA框架，通过快系统直接预测动作、慢系统反思校正，结合连续策略学习，在仿真与真实任务中均提升了操作准确率与校正效率。
摘要：
近年来，已有研究将多模态大语言模型（Multimodal Large Language Models, MLLMs）集成到机器人操作中，构建视觉-语言-动作模型（vision-language-action models, VLAs）以解析多模态信息并预测SE(3)位姿。尽管VLA已取得可观进展，但在面对全新复杂任务时仍可能出现执行失败的问题。为模拟类人推理以实现更鲁棒的操作，我们提出自校正VLA（self-corrected VLA, SC-VLA）框架，该框架在单个VLA策略中整合了用于直接预测动作的快系统，以及用于对失败动作进行反思的慢系统。针对快系统，我们采用参数高效微调方法，在保留MLLM固有推理能力的同时，为模型赋予位姿预测能力。针对慢系统，我们提出一种用于失败校正的思维链训练策略，旨在模拟人类在操作失败后的反思过程。具体而言，我们的模型学习识别动作失败的原因，自适应地寻求专家反馈，对当前失败场景进行反思，并逐步迭代生成校正动作。此外，我们基于成功校正的样本设计了一种连续策略学习方法，以增强快系统对当前配置的适配能力。我们在仿真任务和真实世界任务中将SC-VLA与此前的最优（SOTA）VLA进行对比，结果表明该框架具备高效的校正过程，在已见任务和未见任务上均提升了操作准确率。
英文摘要：
Recently, some studies have integrated Multimodal Large Language Models into robotic manipulation, constructing vision-language-action models (VLAs) to interpret multimodal information and predict SE(3) poses. While VLAs have shown promising progress, they may suffer from failures when faced with novel and complex tasks. To emulate human-like reasoning for more robust manipulation, we propose the self-corrected (SC-)VLA framework, which integrates fast system for directly predicting actions and slow system for reflecting on failed actions within a single VLA policy. For the fast system, we incorporate parameter-efficient fine-tuning to equip the model with pose prediction capabilities while preserving the inherent reasoning abilities of MLLMs. For the slow system, we propose a Chain-of-Thought training strategy for failure correction, designed to mimic human reflection after a manipulation failure. Specifically, our model learns to identify the causes of action failures, adaptively seek expert feedback, reflect on the current failure scenario, and iteratively generate corrective actions, step by step. Furthermore, a continuous policy learning method is designed based on successfully corrected samples, enhancing the fast system's adaptability to the current configuration. We compare SC-VLA with the previous SOTA VLA in both simulation and real-world tasks, demonstrating an efficient correction process and improved manipulation accuracy on both seen and unseen tasks.
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。
