With advances in new engineering education and the rapid evolution of embodied intelligence technologies, conventional robotic teaching platforms are increasingly constrained by limited functionality, strong software–hardware coupling, and poor extensibility for secondary development. Hence, they cannot effectively meet the emerging demands of practical teaching in embodied intelligence, multimodal interaction, and robotic manipulation. To address these challenges, this study designed and developed a dual-arm teleoperated mobile robot experimental platform. An integrated teaching framework encompassing “perception–decision–execution–interaction” was established using this platform to enhance students’ engineering practice and innovation capabilities in robotics and embodied intelligence, while supporting practical teaching and interdisciplinary integration in emerging engineering fields related to embodied intelligence.

The proposed platform has an integrated architecture comprising an omnidirectional mobile chassis, dual robotic arms, and an intelligent interaction module. Its software framework is based on the Robot Operating System (ROS). The platform integrates multiple functional modules, including chassis motion control, SLAM-based mapping and navigation, robotic arm teleoperation, and voice interaction. Multi-sensor data, including LiDAR, IMU, and odometry data, are fused to achieve environmental perception and autonomous motion control. The Cartographer algorithm is employed as the core framework for two-dimensional (2D) map construction for mapping and navigation. Path planning algorithms such as A*, DWA, and TEB are integrated for autonomous navigation and dynamic obstacle avoidance. The teleoperation subsystem employs a master–slave architecture based on socket communication to reproduce the motion trajectories of the dual robotic arms. The voice interaction module enables natural language–based robot control through speech recognition and keyword spotting technologies. Furthermore, a hierarchical experimental teaching system encompassing chassis drive control, ROS communication, mapping and navigation, teleoperation, and voice interaction was established. Practical training was conducted by integrating course-based instruction with exploratory experiments.

Experimental results demonstrated that the proposed platform operated stably with comprehensive functionality and exhibited robust capabilities in autonomous navigation, teleoperation, and intelligent interaction. The mobile chassis achieved smooth omnidirectional motion and rapid control response, and communication between the high-level and low-level controllers remained stable and reliable. The mapping and navigation subsystem successfully generates indoor 2D maps and supports autonomous navigation and dynamic obstacle avoidance under different path planning algorithms. In addition, it enables comparative experiments on various algorithms and parameter configurations. The dual-arm teleoperation subsystem achieves low-latency communication with stable motion synchronization and reproduction. The voice interaction subsystem successfully performs navigation control, operating mode switching, and predefined action triggering. The platform enables students to independently modify navigation parameters, interaction commands, and action sequences, thereby facilitating the transition from verification-based experiments to exploratory experiments. Thus, the platform effectively meets the requirements of robotic function validation and engineering practice-oriented training.

The proposed dual-arm teleoperated mobile robot experimental platform for embodied intelligence education integrates key technologies such as motion control, mapping and navigation, teleoperation, and voice interaction. Thus, a comprehensive experimental teaching system is established to cover low-level drivers, environmental perception, path planning, and intelligent interaction. The platform features high modularity, strong scalability, and cost-effectiveness, demonstrating excellent applicability and reproducibility in educational environments. It effectively cultivates students’ interdisciplinary competencies in embedded systems, robotics, and embodied intelligence. Moreover, it demonstrates strong research extensibility and can serve as an experimental foundation for future studies on imitation learning, reinforcement learning, and embodied intelligence driven by large models.

The significance of this study lies in the conversion of the traditional manufacturing industry into the intelligent, automated transformation of the background to meet the universities and colleges of intelligent manufacturing professional construction and cultivate urgent needs of the new era of composite technical personnel, to help students with the theoretical knowledge for the application of practical training, based on a solid foundation for the workplace, and to mitigate restrictions on the school laboratory space to cultivate diversified professional knowledge of the students.

In this paper, digital twin technology is applied to the intelligent transformation of the modular production system, thereby expanding the practical training content, relying on the “digital twin” virtual simulation technology based on Unigraphics, TIA Portal, PLCSIM Advanced, and other software, to conduct the virtual debugging training of the MPS digital twin model. The Process Simulate software and industrial Internet technology were used to conduct virtual and real linkage training, using intelligent monitoring technology for the real-time monitoring of equipment status, with the support of industrial Internet of Things technology to ensure the stability of data transmission and the safety of the experimental process during the training, reformulating the teaching framework, formulating the teaching plan for practical training in phases, and participating in the students’ competitions.

The teaching reform method of combining digital twin, virtual simulation, virtual-reality interaction, and other technologies with traditional equipment is proposed, and the MPS practical teaching platform is constructed, which integrates mechanical design, control system design, digital twin, and virtual simulation under the teaching platform, improves the comprehensiveness of the practical training teaching, imparts the equipment a higher utilization value while meeting the requirements of talent cultivation, and formulates a teaching framework for combining the content of practical training with college student competitions, which helps students receive several national awards. We have developed a teaching framework that combines practical training content with college student competitions, which has helped students bag several national awards and extended the breadth and depth of intelligent manufacturing practical training education. The application of equipment monitoring technology and industrial internet technology in practical training teaching provides strong support for the implementation of practical training teaching and provides a safe and stable experimental environment for the students.

Combining virtual and real considerably reduces the cost of improvement, effectively enhances the comprehensiveness, design, and research of the experiment, and improves the students’ knowledge of the intelligent manufacturing engineering profession and industrial production, which is highly significant for the cultivation of intelligent manufacturing talents, and concurrently provides a new way of thinking for the reform of practical teaching in colleges and universities and the construction of intelligent manufacturing professions at present. The next step will be to further improve the maturity of the twin model and the degree of intelligence of the equipment to realize a higher degree of digital twins, cultivate more intelligent manufacturing applied talents, and provide a complete practical training platform for students.