Legged robots, especially quadruped robots, are widely used in various environments due to their advantage in overcoming rough terrains. However, falling is inevitable. Therefore, the ability to overcome a falling state is an essential ability for legged robots. In this paper, we propose a method to fully recover a quadruped robot from a fall using a single-neural network model. The neural network model is trained in two steps in simulations using reinforcement learning, and then directly applied to AiDIN-VIII, a quadruped robot with 12 degrees of freedom. Experimental results using the proposed method show that the robot can successfully recover from a fall within 5 s in various postures, even when the robot is completely turned over. In addition, we can see that the robot successfully recovers from a fall caused by a disturbance.

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 effects are strongly amplified. Conventional methods, such as actuator networks or heuristic domain randomization, often require specialized sensors or extensive trial-and-error to tune appropriate randomization ranges. Building on a recent system-identification framework for actuator dynamics, we extend it with an augmented friction model that incorporates the Stribeck effect to capture the low-velocity nonlinearities characteristic of high-gear-ratio actuators. The physical parameters are identified from real-robot trajectory data using an evolutionary algorithm, and the resulting simulation is used to train a locomotion policy that is transferred zero-shot to a 55 kg quadruped without additional fine-tuning or base- or controller-level dynamics randomization. On our platform, adding the Stribeck term lowers the actuator identification error by 16% relative to a Coulomb–Viscous model on the trajectory used for identification, and this advantage generalizes to an unseen trajectory not used for identification. It also lowers the simulation-to-reality mean-velocity degradation from 40.2% and 26.8% for the Coulomb–Viscous model to 27.2% and 21.6% for our method at the 0.3 and 1.0 m/s commands, respectively. The trained policy achieves stable locomotion on flat ground as well as rough terrain including steps and stairs. These results indicate that explicitly modeling low-velocity friction is beneficial for high-fidelity sim-to-real transfer in high-reduction systems.