  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.
