News & Research

With the rapid development of Internet technology and multimedia applications, and the continuous expansion of interactive scenes of humanoid robot education, the number of digital resources of ethnic music has reached an unprecedented scale and continues to grow. This study mainly focuses on the construction of a national music retrieval system that integrates multimodal attention mechanisms and its application implementation in humanoid robot education interaction. Firstly, a method for automatic annotation of ethnic music adapted to multimodal data was studied to meet the human-computer interaction needs of robot education interaction. Secondly, a label conditional random field music automatic annotation method is proposed, and a multi-modal attention mechanism deep neural network model for ethnic music annotation is constructed to enhance the accurate matching of music features and retrieval requirements in human-computer interaction. Finally, integrate the constructed ethnic music retrieval system into the humanoid robot education interactive platform, conduct application analysis based on actual educational interactive scenarios, verify the effectiveness of the system in the human-computer interaction process, and complete performance evaluation. The results indicate that in the Glu module, Glu blocks perform better in ethnic music annotation tasks. The annotation results of various indicators in music hierarchy sequence modeling are superior to traditional models, ensuring the accuracy of ethnic music annotation in human-computer interaction scenarios. Compared with other algorithms, the AUC label score of this system is the highest, reaching 0.913. It can more efficiently model the mapping relationship between multimodal music features input during human-computer interaction and text retrieval requirements. It shows better performance in all evaluation indicators and can effectively support ethnic music retrieval services in humanoid robot education interaction, enhancing the immersion and fun of educational interaction.

This paper presents the mechanical design of a human-ridable giant humanoid robot that enables intuitive pilot-in-the-loop whole-body control. Despite the steady development of humanoid robots, humanoids continue to be constrained by human-scale strength and size, with poor scalability of transmissions and structures at large sizes, and teleoperation limits in latency and situational awareness. To overcome such limitations, we introduce the mechanical design of a human-ridable giant humanoid with a cockpit-style human-machine-interface (HMI): an Internal Robot (IR) with gravity-compensated zero-torque joint stream angles to the External Robot (ER) arms, while the ER lower body provides bipedal stabilization and locomotion. This approach preserves situational awareness, mitigates teleoperation latency and viewpoint issues, and couples human decision making with robotic strength, reach, and endurance. We define the objectives for rideability, biped operation in human environments, and protective, high-tech packaging realized via seated posture, nonwearable manipulator HMI, height-first co-design, rapid low-cost fabrication, and vertical integration. In addition, we detail the subsystem design and demonstrated stable walking and whole-body tasks.