Energy-Efficient Robot Locomotion

The field studies how robots can move farther or perform tasks with less energy, often measured by electrical power or cost of transport. Approaches include reinforcement learning and model-predictive control, terrain-aware energy models, and mechanical designs that exploit compliant feet, springs, passive dynamics, or coordinated actuator use. Researchers test these methods on tasks such as quadruped walking over slopes and rough ground, humanoid gaiting, hopping, and swimming, with simulation and hardware trials used to assess performance. A central challenge is improving efficiency without sacrificing speed, stability, agility, or safety, while ensuring gains observed in simulation or on simple terrain transfer to real robots and varied environments.