A Lightweight Physics-Informed Neural Network for Sim-To-Real of Biped Robot

Abstract

In this paper, we present a low-cost, easy-to-implement sim-to-real framework for biped locomotion that narrows the reality gap using only simulation data, without motion-capture or additional real-world measurements. First, a walking policy for the BRUCE robot is trained in Isaac Gym via reinforcement learning. Next, we develop a compact, physics-informed neural network (PINN) grounded in Euler-Lagrange structure and augmented with an LSTM to predict simulator forward dynamics. Trained solely on simulation trajectories, the PINN forecasts next-step joint angles and velocities of the simulated robot given the physical robot's current state and control inputs. During hardware deployment, and consistent with a whole-body control architecture, these predicted states serve as reference joint states while the policy outputs provide feedforward torque commands; a feedforward-plus-feedback torque controller then computes the executed joint torques, thereby reducing the sim-to-real gap. Experiments on BRUCE demonstrate that our method better reproduces simulated behavior and attains higher tracking accuracy than direct policy transfer. Furthermore, the dynamics predictor runs at 1 kHz on embedded hardware, showing superior real-time performance relative to existing learning-based models.