"Pass the butter": A study on desktop-classic multitasking robotic arm based on advanced YOLOv7 and BERT

TL;DR

This paper presents a desktop-level robotic arm system that integrates advanced YOLOv7, BERT, and speech recognition technologies to enable autonomous decision-making and actions, demonstrating high performance in various tasks.

Contribution

It introduces a novel integration of visual recognition, NLP, and speech recognition on a miniaturized desktop robot for autonomous operation.

Findings

Speech recognition accuracy up to 95.2%

Action performance accuracy up to 84.6%

Feasibility of multimodal integration on edge devices

Abstract

In recent years, various intelligent autonomous robots have begun to appear in daily life and production. Desktop-level robots are characterized by their flexible deployment, rapid response, and suitability for light workload environments. In order to meet the current societal demand for service robot technology, this study proposes using a miniaturized desktop-level robot (by ROS) as a carrier, locally deploying a natural language model (NLP-BERT), and integrating visual recognition (CV-YOLO) and speech recognition technology (ASR-Whisper) as inputs to achieve autonomous decision-making and rational action by the desktop robot. Three comprehensive experiments were designed to validate the robotic arm, and the results demonstrate excellent performance using this approach across all three experiments. In Task 1, the execution rates for speech recognition and action performance were 92.6% and 84.3%, respectively. In Task 2, the highest execution rates under the given conditions reached 92.1% and 84.6%, while in Task 3, the highest execution rates were 95.2% and 80.8%, respectively. Therefore, it can be concluded that the proposed solution integrating ASR, NLP, and other technologies on edge devices is feasible and provides a technical and engineering foundation for realizing multimodal desktop-level robots.