  Robust locomotion control depends on accurate state estimations. However, the
sensors of most legged robots can only provide partial and noisy observations,
making the estimation particularly challenging, especially for external states
like terrain frictions and elevation maps. Inspired by the classical Internal
Model Control principle, we consider these external states as disturbances and
introduce Hybrid Internal Model (HIM) to estimate them according to the
response of the robot. The response, which we refer to as the hybrid internal
embedding, contains the robot's explicit velocity and implicit stability
representation, corresponding to two primary goals for locomotion tasks:
explicitly tracking velocity and implicitly maintaining stability. We use
contrastive learning to optimize the embedding to be close to the robot's
successor state, in which the response is naturally embedded. HIM has several
appealing benefits: It only needs the robot's proprioceptions, i.e., those from
joint encoders and IMU as observations. It innovatively maintains consistent
observations between simulation reference and reality that avoids information
loss in mimicking learning. It exploits batch-level information that is more
robust to noises and keeps better sample efficiency. It only requires 1 hour of
training on an RTX 4090 to enable a quadruped robot to traverse any terrain
under any disturbances. A wealth of real-world experiments demonstrates its
agility, even in high-difficulty tasks and cases never occurred during the
training process, revealing remarkable open-world generalizability.
