Joint optimization framework for robot dance motion generation, aesthetic evaluation, and dynamic balance control based on conditional gans and safe deep reinforcement learning

Artificial intelligence and intelligent algorithms provide effective technical support for generating complex robotic dance motions and maintaining stable execution under dynamic constraints. To address the separation among motion synthesis, aesthetic evaluation, and balance control in existing robot dance systems, this study proposes a joint optimization framework that integrates a conditional Generative Adversarial Network (cGAN), multimodal aesthetic scoring, and safe Deep Reinforcement Learning (DRL). A temporal motion representation model is first constructed to encode joint angles, angular velocities, center-of-mass trajectories, rhythm labels, and style conditions. The cGAN generates style-consistent dance sequences through content encoding, style embedding, and sequence decoding, while a mechanical safety filter constrains joint angles, torque limits, self-collision, and support-region feasibility. A multimodal aesthetic evaluation model is then developed by fusing visual posture features, motion smoothness, spatial symmetry, and acoustic rhythm features. Human expert ratings are introduced to calibrate the automated aesthetic score and validate its objectivity. At the control level, a PPO-based policy is trained with ZMP constraints, joint-limit penalties, energy penalties, and safety-priority reward weighting. All baseline models, including LSTM-GAN, Transformer-GAN, MotionGAN, PID, DDPG, PPO, and TD3, are trained under identical data partitions and evaluation protocols. Experimental results show that the proposed framework reduces FID to 24.8, improves style recognition accuracy to 91.7%, increases aesthetic-score correlation with expert ratings to 0.86, and lowers the fall rate to 1.2%. Ablation and robustness tests further confirm the contribution of style transfer, multimodal feature fusion, PPO control, and ZMP-based safety constraints under external disturbances, sensor noise, uneven ground, and high-complexity dance motions.