  Precise trajectory tracking for legged robots can be challenging due to their
high degrees of freedom, unmodeled nonlinear dynamics, or random disturbances
from the environment. A commonly adopted solution to overcome these challenges
is to use optimization-based algorithms and approximate the system with a
simplified, reduced-order model. Additionally, deep neural networks are
becoming a more promising option for achieving agile and robust legged
locomotion. These approaches, however, either require large amounts of onboard
calculations or the collection of millions of data points from a single robot.
To address these problems and improve tracking performance, this paper proposes
a method based on iterative learning control. This method lets a robot learn
from its own mistakes by exploiting the repetitive nature of legged locomotion
within only a few trials. Then, a torque library is created as a lookup table
so that the robot does not need to repeat calculations or learn the same skill
over and over again. This process resembles how animals learn their muscle
memories in nature. The proposed method is tested on the A1 robot in a
simulated environment, and it allows the robot to pronk at different speeds
while precisely following the reference trajectories without heavy
calculations.
