Enhancing Sim-to-Real Transfer for a High-Gear-Ratio Quadruped Robot via Extended Actuator Dynamics Identification
Reinforcement learning (RL) has become a powerful tool for quadrupedal locomotion, and a sim-to-real approach is widely adopted to avoid hardware damage during training. However, the “sim-to-real gap” remains a critical challenge, particularly for robots driven by high-gear-ratio actuators, in which nonlinear friction...