基于多评论家模仿学习的人形机器人运动技能生成方法

Whole-body motion skills are fundamental for humanoid robots to achieve complex behaviors and perform diverse tasks. Although existing motion control methods perform well in simulation, transferring them to the real world remains challenging. Humanoid robots typically have high degrees of freedom and strong dynamic coupling. As a result, accurate motion imitation and stable sim-to-real transfer are difficult to achieve under dynamics discrepancies and sensor noise. To address these issues, this research proposes a humanoid motion skill learning method based on multi-critic actor imitation learning. First, a difficulty-aware curriculum learning strategy based on a motion complexity metric is designed, enabling the robot to progressively learn complex motion skills from diverse motion datasets. Then, a multi-critic actor network architecture is introduced to improve the fidelity of imitation to motion capture data while balancing motion imitation accuracy and sim-to-real transfer capability. Experimental results show that the proposed method achieves high-precision motion imitation on the Unitree G1 humanoid robot. Motion skills learned in simulation are successfully transferred to the real world in a zero-shot setting. This demonstrates the effectiveness and generalization capability of the proposed method in real-world scenarios.