Learning Multiple Gaits within Latent Space for Quadruped Robots

Understand

Learning multiple gaits is non-trivial for legged robots, especially when encountering different terrains and velocity commands.

• In this work, we present an end-to-end training framework for learning multiple gaits for quadruped robots, tailored to the needs of robust locomotion, agile locomotion, and user's commands.
• A latent space is constructed concurrently by a gait encoder and a gait generator, which helps the agent to reuse multiple gait skills to achieve adaptive gait behaviors.
• To learn natural behaviors for multiple gaits, we design gait-dependent rewards that are constructed explicitly from gait parameters and implicitly from conditional adversarial motion priors (CAMP).