Deep Learning-Based Contact Force Control for a Robotic Leg.

This paper proposes a learning-based contact force controller using deep neural networks (DNN) and a PI controller. Stable contact force control between the foot and the ground is essential for humanoid robots to maintain balance during bipedal walking. While admittance controllers have been extensively employed for contact force control in humanoid robots, their performance is limited by the high nonlinearity inherent in robot systems. To overcome these limitations, we propose a deep neural network (DNN)-based inverse model, which leverages input-output data that inherently capture system nonlinearities. The proposed learning-based contact force controller computes the target foot height based on the target force, measured force, and measured foot height, without relying on a dynamic model of the articulated robotic leg. Furthermore, a PI controller is integrated to mitigate steady-state errors. Experimental comparisons between the proposed controller and an admittance controller were conducted using an articulated robotic leg. Compared with an admittance controller, the proposed method reduced overshoot by 96% and settling time by 61% on average in step responses and decreased force-tracking RMSE by 66.3% on average across both step and sinusoidal experiments.