  In reinforcement learning for legged robot locomotion, crafting effective
reward strategies is crucial. Pre-defined gait patterns and complex reward
systems are widely used to stabilize policy training. Drawing from the natural
locomotion behaviors of humans and animals, which adapt their gaits to minimize
energy consumption, we propose a simplified, energy-centric reward strategy to
foster the development of energy-efficient locomotion across various speeds in
quadruped robots. By implementing an adaptive energy reward function and
adjusting the weights based on velocity, we demonstrate that our approach
enables ANYmal-C and Unitree Go1 robots to autonomously select appropriate
gaits, such as four-beat walking at lower speeds and trotting at higher speeds,
resulting in improved energy efficiency and stable velocity tracking compared
to previous methods using complex reward designs and prior gait knowledge. The
effectiveness of our policy is validated through simulations in the IsaacGym
simulation environment and on real robots, demonstrating its potential to
facilitate stable and adaptive locomotion.
