Shake-VLA: 視覚言語行動モデルによる双腕ロボットのカクテル調製システム
Shake-VLA: Vision-Language-Action Model-Based System for Bimanual Robotic Manipulations and Liquid Mixing
視覚・音声・言語モデルを統合したVLAシステムで、双腕ロボットが材料の検出・計量・混合作業を行い、カクテルを自動調製する。
著者: Muhamamd Haris Khan, Selamawit Asfaw, Dmitrii Iarchuk, Miguel Altamirano Cabrera, Luis Moreno, Issatay Tokmurziyev, Dzmitry Tsetserukou
分類: cs.RO
原文アブストラクト
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.
