Accurate power consumption estimation method makes walking robots energy efficient and quiet

Power consumption is a frequently over-looked aspect in robotics, especially in the context of legged robots. Nevertheless, improving the efficiency of walking robots is crucial to overcome the current limitations in runtime. This work proposes a novel method for precisely estimating actuator power consumption based on LSTM neural networks. The performance of this approach is benchmarked against currently employed models and validated on real hardware using certified instruments. The proposed method is integrated into the Isaac Gym framework and utilized to train a power-efficient policy. Instead of optimizing for handcrafted cost functions, such as the often used torque-square minimization, our approach for the first time trains RL policies that minimize the effective energy consumption. Hardware results demonstrate a reduction of approximately 25% in the robot’s total power consumption, with a notable 50% decrease observed for the knee actuator. Additionally, the newly developed policy generates significantly smoother and quieter motions.