  Recent years have witnessed many successful trials in the robot learning
field. For contact-rich robotic tasks, it is challenging to learn coordinated
motor skills by reinforcement learning. Imitation learning solves this problem
by using a mimic reward to encourage the robot to track a given reference
trajectory. However, imitation learning is not so efficient and may constrain
the learned motion. In this paper, we propose instruction learning, which is
inspired by the human learning process and is highly efficient, flexible, and
versatile for robot motion learning. Instead of using a reference signal in the
reward, instruction learning applies a reference signal directly as a
feedforward action, and it is combined with a feedback action learned by
reinforcement learning to control the robot. Besides, we propose the action
bounding technique and remove the mimic reward, which is shown to be crucial
for efficient and flexible learning. We compare the performance of instruction
learning with imitation learning, indicating that instruction learning can
greatly speed up the training process and guarantee learning the desired motion
correctly. The effectiveness of instruction learning is validated through a
bunch of motion learning examples for a biped robot and a quadruped robot,
where skills can be learned typically within several million steps. Besides, we
also conduct sim-to-real transfer and online learning experiments on a real
quadruped robot. Instruction learning has shown great merits and potential,
making it a promising alternative for imitation learning.
