Image Recognition by In-context Learning using LLM for Robotic Picking Tasks

Abstract

This research proposes a method using Large Language Model (LLM) to derive appropriate text prompts for image recognition tasks under various conditions. This method leverages In-context Learning and Few-shot Prompting to enable LLM to understand tasks based on provided data and execute them with minimal examples. Experiments on image recognition in logistics robot picking scenes confirmed the method's effectiveness in deriving suitable prompts for diverse scenarios, multi-product situations and including shrink-wrapped items.