Shake-VLA：基于视觉-语言-动作模型的双臂机器人操作与液体混合系统
Shake-VLA: Vision-Language-Action Model-Based System for Bimanual Robotic Manipulations and Liquid Mixing
AI总结：
本研究提出基于VLA模型的Shake-VLA双臂鸡尾酒调制系统，集成多模块实现配料检测、指令解析与精准操作，实验验证各组件及整体调制均具高成功率。
摘要：
本文介绍了Shake-VLA，这是一种基于视觉-语言-动作（Vision-Language-Action，VLA）模型的系统，旨在实现双臂机器人操作以自动调制鸡尾酒。该系统集成了用于检测配料瓶并读取标签的视觉模块、用于解读用户指令的语音转文本模块，以及用于生成任务特定机器人指令的语言模型。系统采用力扭矩（Force Torque，FT）传感器精确测量倒出的液体量，确保混合过程中配料比例的准确性。系统架构包含用于获取和适配配方的检索增强生成（Retrieval-Augmented Generation，RAG）模块、用于解决配料可用性问题的异常检测机制，以及用于灵巧操作的双臂机械臂。实验评估显示各系统组件均具有较高成功率：语音转文本模块在嘈杂环境中成功率达93%，视觉模块在杂乱环境中目标与标签检测成功率达91%，异常模块成功识别出95%的检测配料与配方要求不符的情况，系统在从配方制定到动作生成的鸡尾酒调制全流程中整体成功率达100%。
英文摘要：
This paper introduces Shake-VLA, a Vision-Language-Action (VLA) model-based system designed to enable bimanual robotic manipulation for automated cocktail preparation. The system integrates a vision module for detecting ingredient bottles and reading labels, a speech-to-text module for interpreting user commands, and a language model to generate task-specific robotic instructions. Force Torque (FT) sensors are employed to precisely measure the quantity of liquid poured, ensuring accuracy in ingredient proportions during the mixing process. The system architecture includes a Retrieval-Augmented Generation (RAG) module for accessing and adapting recipes, an anomaly detection mechanism to address ingredient availability issues, and bimanual robotic arms for dexterous manipulation. Experimental evaluations demonstrated a high success rate across system components, with the speech-to-text module achieving a 93% success rate in noisy environments, the vision module attaining a 91% success rate in object and label detection in cluttered environment, the anomaly module successfully identified 95% of discrepancies between detected ingredients and recipe requirements, and the system achieved an overall success rate of 100% in preparing cocktails, from recipe formulation to action generation.
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。
