## 2 Related Research  
### Review of Related Research  
Research on human motion generation has focused on improving performance, preventing injury, and designing robot control systems that replicate human motion. Two primary methods for motion generation have emerged: data-driven approaches, which leverage machine learning and AI, and model-based approaches, such as dynamic optimization.  
Data-driven motion generation can accurately replicate movements from actual data. Deep reinforcement learning and neural network models have been successfully employed to generate motion for new tasks [7, 8]. However, a significant limitation of these methods is that the generation process is often black-boxed [9, 10], rendering it difficult to understand the underlying mechanisms or to represent them mathematically.  
Model-based motion generation provides more accurate simulations, as the models are grounded in physical laws and physiological knowledge. This approach enables the quantitative interpretation of motion based on simulation results. However, it requires expert knowledge and careful validation of the model design, tuning, and understanding of its representation limits. Whole-body musculoskeletal models have been utilized to understand human motion by quantifying movements through joint angle and torque calculations [4, 5]. These models enable the analysis of athletic performance and the estimation of internal loads on the musculoskeletal system. However, they may be unsuitable for real-time processing owing to their long computation times. To address this limitation, simplified models that exclude less impactful elements have been proposed. The inverted pendulum model is widely used to reproduce human locomotion as a simple model to represent human locomotion. The realistic simulation of human motion has been applied to animation generation [11, 12] and robot hopping and movement [13, 14, 15]. Among them, many valuable insights regarding the control of biped robots have been obtained, including a new approach to the dynamic control of robot running and hopping [16], and a control method based on dynamic optimization aimed at enhancing robot stability and efficiency [17].  
Many studies on motion generation have employed dynamic optimization. [PERSON] et al. [18] demonstrated the generation of typical muscle activity during walking through dynamic optimization, which accounted for energy consumption, to predict muscle activity during the flight phase. Similarly, [PERSON] et al. [19] focused on movement economy by minimizing metabolic energy expenditure per unit distance traveled, resulting in simulation outcomes that closely resembled actual motion characteristics motion.  
In optimization, multiple evaluation criteria, rather than a single criterion, have been explored to generate more human-like motion [6; 20; 21; 22; 23]. [PERSON] et al. [24] utilized energy consumption and motion smoothness as weighted evaluation functions for motion generation, which resulted in sprinter-like running patterns. [PERSON] et al. [25] proposed an inverse optimization control method that derives the weights of basis functions in the motion generation optimization problem from actual motion data, enabling the generation of motion paths that closely mimic actual human behavior.