Efficient learning of robust multigait quadruped locomotion for minimizing the cost of transport

• Proposes a training–synthesizing framework that integrates learned gait-conditioned policies into a single multiskill locomotion policy. • Achieves low-cost, smooth gait switching and controllable gaits via reinforcement learning without manual tuning. • Demonstrates seamless gait transitions while maintaining energy optimality across all velocity commands. • Addresses energy efficiency, robustness, and hardware safety in learned multigait control for quadruped robots.