As a fundamental sports discipline, track and field not In recent years, real-time monitoring and data analysis only forms the core of major events like the Olympics have become increasingly critical in enhancing athletic and World Championships but also plays a crucial role in performance. Studies have shown that by monitoring physiological promoting public health Jacobsson, Ekberg, Timpka, Haggren indicators (such as heart rate, body temperature, and Råsberg, Sjöberg, Mirkovic and Nilsson (2020); Timpka, blood oxygen saturation) and performance metrics (such as Dahlström, Fagher, Adami, Andersson, Jacobsson, Svedin speed, acceleration, and force) in real-time, it is possible to and Bermon (2022). The wide variety of track and field events, identify problems during training promptly and make targeted including sprints, middle and long-distance running, jumps, adjustments. For example, analyzing heart rate changes under and throws, demand high levels of physical fitness, technical different training intensities can assess endurance levels and skills, and mental strength from athletes Guo (2022); Zhang recovery status, while monitoring gait and acceleration during et al. (2023a). To excel in such competitive environments, running can optimize technical movements and improve athletes require not only innate talent and dedication but efficiency Rana and Mittal (2020a). Many studies have begun also scientific and systematic training methods Zhang et al. exploring the potential of using sensor technology and data (2023b); Yuan et al. (2024).

Acquiring human skills offers an efficient approach to tackle complex task planning challenges. When performing a learned skill model for a continuous contact task, such as robot polishing in an uncertain environment, the robot needs to be able to adaptively modify the skill model to suit the environment and perform the desired task. The environmental perturbation of the polishing task is mainly reflected in the variation of contact force. Therefore, adjusting the task skill model by providing feedback on the contact force deviation is an effective way to meet the task requirements. In this study, a phase-modulated diagonal recurrent neural network (PMDRNN) is proposed for force feedback model learning in the robotic polishing task. The contact between the tool and the workpiece in the polishing task can be considered a dynamic system. In comparison to the existing feedforward neural network phase-modulated neural network (PMNN), PMDRNN combines the diagonal recurrent network structure with the phase-modulated neural network layer to improve the learning performance of the feedback model for dynamic systems. Specifically, data from real-world robot polishing experiments are used to learn the feedback model. PMDRNN demonstrates a significant reduction in the training error of the feedback model when compared to PMNN. Building upon this, the combination of PMDRNN and dynamic movement primitives (DMPs) can be used for real-time adjustment of skills for polishing tasks and effectively improve the robustness of the task skill model. Finally, real-world robotic polishing experiments are conducted to demonstrate the effectiveness of the approach.