This paper proposes an industrial humanoid robot system that integrates a new type of bionic joint mechanism design and a lightweight visual perception decision-making algorithm to address the two core scientific issues of insufficient dynamic response of humanoid robot joints in sports training scenarios and the imbalance between real-time and accuracy of visual guidance interaction. This provides an optimization solution for the human-machine collaboration problem of training assistive humanoid robots. At the hardware level, innovative design of a 7-degree-of-freedom upper limb bionic joint based on harmonic reducers and frameless torque motors. By optimizing structural parameters to solve the coupling contradiction between joint torque and response speed, the maximum output torque of the shoulder joint reached 42.3 N · m, and the step response time was compressed to 62 ms. At the algorithm level, a lightweight LSTM fusion pipeline was proposed to break through the accuracy speed trade-off bottleneck of traditional visual algorithms in motion posture recognition and prediction. On the self built SportsPace-2025 dataset, a motion recognition accuracy of 92.4% and a 3D posture estimation error of 48.7mm were achieved, with an average end-to-end delay of 168ms, meeting real-time interaction requirements. User experiments have shown that after two weeks of training with the system, the standardized score of the badminton swing motion of the humanoid robot subjects increased from 5.2 to 7.8 (p<0.01), significantly better than the control group. The research has verified the effectiveness of the proposed structural design and algorithm framework in enhancing the training assistance capability of humanoid robots and the naturalness of human-machine interaction, providing new methods for the engineering application of humanoid robots in motion scenes.
Wang et al. (Thu,) studied this question.
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